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第171行：
第171行： − + − + − + − + − + − + − + 第219行：
第219行： − + 第287行：
第287行： − 情感数据库的建立是一项既困难又耗时的工作。然而，创建数据库是创建识别人类情感的系统的关键步骤。大多数公开的情感数据库只包含摆拍的面部表情，在这样的数据库中，参与者被要求展示不同的基本情绪表情；而在自然表情数据库中，面部表情是自发的。自然表情的发生需要选取恰当的刺激，这样才能引起目标表情的丰富展示。其次，这个过程需要受过训练的工作者为数据做标注，以实现数据库的高度可靠。因为表情及其强度的感知本质上是主观的，专家的标注对验证而言是十分重要的。+ 第306行：
第306行： − + 第356行：
第356行： − 然而，在20世纪90年代，埃克曼扩展了他的基本情绪列表，包括一系列积极和消极的情绪，这些情绪并非都编码在面部肌肉中。新增的情绪是: + 第370行：
第370行： − + 第376行：
第376行： − + + + 第389行：
第391行： − + 第412行：
第414行： − + − + − + − + − + − + − + − + − <nowiki>-| 快乐 | | 6 + 12 |-| 悲伤 | | 1 + 4 + 15 |-| 惊喜 | | 1 + 2 + 5 b + 26 |-| 恐惧 | | 1 + 2 + 4 + 5 + 20 + 26 |-愤怒 | 4 + 5 + 7 + 23 |-| 厌恶 | 9 + 15 + 16 |-| 藐视 | R12A + R14A | }</nowiki> − − + − 正如每一个计算实践，在人脸处理的情感检测中，一些障碍需要被超越，以便充分释放所使用的整体算法或方法的隐藏潜力。在几乎所有基于人工智能的检测(语音识别、人脸识别、情感识别)的早期，建模和跟踪的准确性一直是个问题。随着硬件的发展，随着更多的数据被收集，随着新的发现和新的实践的引入，这种缺乏准确性的现象逐渐消失，留下了噪音问题。然而，现有的去噪方法包括邻域平均法、线性高斯平滑法、中值滤波法【32】，或者更新的方法如细菌觅食优化算法【33】【34】。+ 第454行：
第454行： − 其他的挑战 + − + + + + + + + + 第464行：
第471行： − 姿势可以有效地作为一种检测用户特定情绪状态的手段，特别是与语音和面部识别结合使用时。根据具体的动作，姿势可以是简单的反射性反应，比如当你不知道一个问题的答案时抬起你的肩膀，或者它们可以是复杂和有意义的，比如当用手语交流时。不需要利用任何物体或周围环境，我们可以挥手、拍手或招手。另一方面，当我们使用物体时，我们可以指向它们，移动，触摸或者处理它们。计算机应该能够识别这些，分析上下文，并以一种有意义的方式作出响应，以便有效地用于人机交互。+ 第470行：
第477行： − 提出了许多的方法来检测身体姿势【36】。 一些文献区分了姿势识别的两种不同方法：基于 3D 模型和基于外观【37】。最重要的方法是利用人体关键部位的三维信息，获得手掌位置、关节角度等重要参数。另一方面，基于外观的系统使用图像或视频进行直接解释。手势一直是身体姿态检测方法的共同焦点【37】。+ + + − 这可用于通过监测和分析用户的生理迹象来检测用户的情感状态。 这些迹象的范围从心率和皮肤电导率的变化到面部肌肉的微小收缩和面部血流的变化。这个领域的发展势头越来越强劲，我们现在看到了实现这些技术的真正产品。通常被分析的4个主要生理特征是血容量脉搏、皮肤电反应、面部肌电图和面部颜色模式。+ + + − Blood volume pulse+ − + − − 一个实验对象的血容量脉搏(BVP)可以通过一个叫做光容血管造影术的技术来测量，这个过程产生一个图表来显示通过四肢的血液流动【38】。波峰表明心脏将血液泵入四肢的心动周期。如果受试者感到恐惧或受到惊吓，他们的心脏通常会“跳动”并快速跳动一段时间，导致心脏周期的振幅增加。当波谷和波峰之间的距离减小时，可以在光电容积描记器上清楚地看到这一点。当受试者平静下来，身体内核扩张，允许更多的血液回流到四肢，循环将恢复正常。+ − + − + − − − − − 第502行：
第507行： − 红外光通过特殊的传感器硬件照射在皮肤上，测量皮肤反射的光量。反射和透射光的数量与 BVP 相关，因为光线被血红蛋白吸收，而血液中的血红蛋白含量丰富。+ − + − + − − − 第515行：
第517行： − 确保发出红外光并监测反射光的传感器始终指向同一个末端可能很麻烦，尤其是在使用计算机时观察对象经常伸展并重新调整其位置时。 还有其他因素会影响一个人的血容量脉搏。因为它是通过四肢的血流量的量度，如果受试者感觉热，或特别冷，那么他们的身体可能允许更多或更少的血液流向四肢，所有这一切都与受试者的情绪状态无关。+ + + 第530行：
第534行： − + + + 第541行：
第547行： − + 第548行：
第554行： − − − + + + 第561行：
第567行： − + + + 第573行：
第581行： + + − 美学，在艺术和摄影界，是指自然和欣赏美的原则。 判断美和其他审美品质是一项高度主观的任务。 宾夕法尼亚州立大学的计算机科学家将使用视觉内容自动推断图片的美学质量的挑战视为机器学习问题，并将同行评议的在线照片共享网站作为数据源【43】。 他们根据直觉提取某些视觉特征，即他们可以区分美学上令人愉悦的图像和令人不快的图像。+ + + + + + 第590行：
第605行： + + − 社会机器人，以及越来越多的机器人在医疗保健中的应用都受益于情感意识，因为它们可以更好地判断用户和病人的情感状态，并适当地改变他们的行为/编程。在人口老龄化日益严重和/或缺乏年轻工人满足其需要的国家，这一点尤为重要【45】。+ 第601行：
第618行： + − + − 第619行：
第636行： − 情感视频游戏可以通过生物反馈设备访问玩家的情绪状态【48】。一种特别简单的生物反馈形式可以通过游戏手柄来测量按下按钮的压力：这已被证明与玩家的唤醒水平密切相关【49】； 另一方面是脑机接口【50】【51】。情感游戏已被用于医学研究，以支持自闭症儿童的情感发展【52】。+ − − − + − 其他潜在的应用主要围绕社会监控。例如，一辆汽车可以监控所有乘客的情绪，并采取额外的安全措施，例如，如果发现司机生气，就向其他车辆发出警报【53】。情感计算在人机交互方面有着潜在的应用，比如情感镜子可以让用户看到自己的表现; 情感监控代理在发送愤怒邮件之前发送警告; 甚至音乐播放器可以根据情绪选择音轨【54】。+ 第635行：
第650行： − + 第641行：
第656行： − 人们也可以利用情感状态识别来判断电视广告的影响，通过实时录像和随后对他或她的面部表情的研究。对大量主题的结果进行平均，我们就能知道这个广告(或电影)是否达到了预期的效果，以及观众最感兴趣的元素是什么。+ + − + − − + 第655行：
第670行： − 皮卡德的研究重点是人机交互，她研究情感计算的目标是“赋予计算机识别、表达、在某些情况下‘拥有’情感的能力”【4】。相比之下，交互式的方法旨在帮助“人们理解和体验他们自己的情绪”【57】，并改善以电脑为媒介的人际沟通。它不一定寻求将情感映射到机器解释的客观数学模型中，而是让人类以可能含糊不清、主观且对上下文敏感的开放式方式理解彼此的情感表达【57】。+ 第661行：
第676行： − 皮卡德的批评者将她的情感概念描述为“客观的、内在的、私人的和机械的”。他们认为它把情绪简化为发生在身体内部的一个离散的心理信号，这个信号可以被测量，并且是认知的输入，削弱了情绪体验的复杂性。+ 第667行：
第682行： − 互动方法断言，虽然情绪具有生物物理方面，但它是“以文化为基础的，动态体验的，并在某种程度上构建于行动和互动中”【57】。换句话说，它认为“情感是一种通过我们的互动体验到的社会和文化产品”【57】【58】【59】。+ − − −
全文文本润色
* LDC-特征以向量形式表示，通过计算特征的线性组合来分类。
* LDC：特征以向量形式表示，通过计算特征的线性组合来分类。
* k-NN-计算并选取特征空间中的点，将其与k个最近的数据点相比较，频数最大的类即为分类结果。
* k-NN：计算并选取特征空间中的点，将其与k个最近的数据点相比较，频数最大的类即为分类结果。
* GMM-是一种概率模型，用于表示总体中子群的存在。 利用特征的多个高斯概率密度函数混合来分类【21】。
* GMM：是一种概率模型，用于表示总体中子群的存在。 利用特征的多个高斯概率密度函数混合来分类【21】。
* SVM-是一种(通常为二分的)线性分类器，它决定每个输入可能属于两个(或多个)可能类别中的哪一个。
* SVM：是一种(通常为二分的)线性分类器，它决定每个输入可能属于两个(或多个)可能类别中的哪一个。
* ANN-是一种受生物神经网络启发的数学模型，能够更好地处理特征空间可能存在的非线性。
* ANN：是一种受生物神经网络启发的数学模型，能够更好地处理特征空间可能存在的非线性。
* 决策树算法——在一颗树中，每个叶子结点都是一个分类点，分支（路径）代表了一系列相邻接的特征，最终引向叶子节点实现分类。
* 决策树算法：在一颗树中，每个叶子结点都是一个分类点，分支（路径）代表了一系列相邻接的特征，最终引向叶子节点实现分类。
* HMMs-一种统计马尔可夫模型，其中的状态和状态转变不能直接用于观测。相反，依赖于状态的一系列输出是可见的。在情感识别领域，输出代表了语音特征向量的序列，这样可以推导出模型所经过的状态序列。这些状态包括情感表达中的各中间步骤，每个状态在输出向量上都有一个概率分布。状态序列是我们能够预测正在试图分类的情感状态，这也是语音情感识别中最为常用的技术之一。
* HMMs：一种统计马尔可夫模型，其中的状态和状态转变不能直接用于观测。相反，依赖于状态的一系列输出是可见的。在情感识别领域，输出代表了语音特征向量的序列，这样可以推导出模型所经过的状态序列。这些状态包括情感表达中的各中间步骤，每个状态在输出向量上都有一个概率分布。状态序列是我们能够预测正在试图分类的情感状态，这也是语音情感识别中最为常用的技术之一。
It is proved that having enough acoustic evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM-RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network. The proposed variant achieves better performance than the other two sets of classifiers.<ref>{{cite journal|url=http://ntv.ifmo.ru/en/article/11200/raspoznavanie_i_prognozirovanie_dlitelnyh__emociy_v_rechi_(na_angl._yazyke).htm|title=Extended speech emotion recognition and prediction|author=S.E. Khoruzhnikov|journal=Scientific and Technical Journal of Information Technologies, Mechanics and Optics|volume=14|issue=6|page=137|year=2014|display-authors=etal}}</ref>
It is proved that having enough acoustic evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM-RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network. The proposed variant achieves better performance than the other two sets of classifiers.<ref>{{cite journal|url=http://ntv.ifmo.ru/en/article/11200/raspoznavanie_i_prognozirovanie_dlitelnyh__emociy_v_rechi_(na_angl._yazyke).htm|title=Extended speech emotion recognition and prediction|author=S.E. Khoruzhnikov|journal=Scientific and Technical Journal of Information Technologies, Mechanics and Optics|volume=14|issue=6|page=137|year=2014|display-authors=etal}}</ref>
The complexity of the affect recognition process increases with the number of classes (affects) and speech descriptors used within the classifier. It is, therefore, crucial to select only the most relevant features in order to assure the ability of the model to successfully identify emotions, as well as increasing the performance, which is particularly significant to real-time detection. The range of possible choices is vast, with some studies mentioning the use of over 200 distinct features. It is crucial to identify those that are redundant and undesirable in order to optimize the system and increase the success rate of correct emotion detection. The most common speech characteristics are categorized into the following groups.
The complexity of the affect recognition process increases with the number of classes (affects) and speech descriptors used within the classifier. It is, therefore, crucial to select only the most relevant features in order to assure the ability of the model to successfully identify emotions, as well as increasing the performance, which is particularly significant to real-time detection. The range of possible choices is vast, with some studies mentioning the use of over 200 distinct features. It is crucial to identify those that are redundant and undesirable in order to optimize the system and increase the success rate of correct emotion detection. The most common speech characteristics are categorized into the following groups.
情感识别过程的复杂性随着分类器中使用的类(情感)和语音叙词的数量的增加而增加。因此，为了保证模型能够成功地识别情绪，并提高性能，只选择最相关的特征是至关重要的，这对于实时检测尤为重要。可选择范围很广，有些研究提到使用了200多种不同的特征【20】。识别冗余的情感信息对于优化系统、提高情感检测的成功率至关重要。最常见的言语特征可分为以下几类【24】【25】。
情感识别过程的复杂性随着分类器中使用的类(情感)和语音叙词的数量的增加而增加。因此，为了保证模型能够成功地识别情绪并提高性能，只选择最相关的特征，这对于实时检测尤为重要。可选择范围很广，有些研究提到使用了200多种不同的特征【20】。识别冗余的情感信息对于优化系统、提高情感检测的成功率至关重要。最常见的言语特征可分为以下几类【24】【25】。
# Frequency characteristics<ref>{{Cite book |doi=10.1109/ICCCI50826.2021.9402569|isbn=978-1-7281-5875-4|chapter=Non-linear frequency warping using constant-Q transformation for speech emotion recognition|title=2021 International Conference on Computer Communication and Informatics (ICCCI)|pages=1–4|year=2021|last1=Singh|first1=Premjeet|last2=Saha|first2=Goutam|last3=Sahidullah|first3=Md|arxiv=2102.04029}}</ref>
# Frequency characteristics<ref>{{Cite book |doi=10.1109/ICCCI50826.2021.9402569|isbn=978-1-7281-5875-4|chapter=Non-linear frequency warping using constant-Q transformation for speech emotion recognition|title=2021 International Conference on Computer Communication and Informatics (ICCCI)|pages=1–4|year=2021|last1=Singh|first1=Premjeet|last2=Saha|first2=Goutam|last3=Sahidullah|first3=Md|arxiv=2102.04029}}</ref>
Creation of an emotion database is a difficult and time-consuming task. However, database creation is an essential step in the creation of a system that will recognize human emotions. Most of the publicly available emotion databases include posed facial expressions only. In posed expression databases, the participants are asked to display different basic emotional expressions, while in spontaneous expression database, the expressions are natural. Spontaneous emotion elicitation requires significant effort in the selection of proper stimuli which can lead to a rich display of intended emotions. Secondly, the process involves tagging of emotions by trained individuals manually which makes the databases highly reliable. Since perception of expressions and their intensity is subjective in nature, the annotation by experts is essential for the purpose of validation.
Creation of an emotion database is a difficult and time-consuming task. However, database creation is an essential step in the creation of a system that will recognize human emotions. Most of the publicly available emotion databases include posed facial expressions only. In posed expression databases, the participants are asked to display different basic emotional expressions, while in spontaneous expression database, the expressions are natural. Spontaneous emotion elicitation requires significant effort in the selection of proper stimuli which can lead to a rich display of intended emotions. Secondly, the process involves tagging of emotions by trained individuals manually which makes the databases highly reliable. Since perception of expressions and their intensity is subjective in nature, the annotation by experts is essential for the purpose of validation.
情感数据库的建立是一项既困难又耗时的工作。然而，情感数据库是创建识别人类情感的系统的关键步骤。大多数公开的情感数据库只包含摆拍的面部表情，在这样的数据库中，参与者被要求展示不同的基本情绪表情；而在自然表情数据库中，面部表情是自发的。自然表情的发生需要选取恰当的刺激，这样才能引起目标表情的丰富展示。其次，这个过程需要受过训练的工作者为数据做标注，以实现数据库的高度可靠。因为表情及其强度的感知本质上是主观的，专家的标注对验证而言是十分重要的。
Researchers work with three types of databases, such as a database of peak expression images only, a database of image sequences portraying an emotion from neutral to its peak, and video clips with emotional annotations. Many facial expression databases have been created and made public for expression recognition purpose. Two of the widely used databases are CK+ and JAFFE.
Researchers work with three types of databases, such as a database of peak expression images only, a database of image sequences portraying an emotion from neutral to its peak, and video clips with emotional annotations. Many facial expression databases have been created and made public for expression recognition purpose. Two of the widely used databases are CK+ and JAFFE.
He therefore officially put forth six basic emotions, in 1972:
He therefore officially put forth six basic emotions, in 1972:
二十世纪六十年代末，保罗·埃克曼 (Paul Ekman) 在巴布亚新几内亚的 Fore Tribesmen 上进行跨文化研究，提出了一种观点，即情感的面部表情不是由文化决定的，而是普遍存在的。因此，他认为它们是起源于生物的，能够可靠地分类。 因此，他在 1972 年正式提出了六种基本情绪【29】：
二十世纪六十年代末，保罗·埃克曼 (Paul Ekman) 在巴布亚新几内亚的法雷人部落（ Fore Tribesmen） 上进行跨文化研究，提出了一种观点，即情感所对应的面部表情不是由文化决定的，而是普遍存在的。因此，他认为面部表情是生物本能，能够可靠地分类。 因此，他在 1972 年正式提出了六种基本情绪【29】：
* [[Anger]]
* [[Anger]]
# Shame
# Shame
然而，在20世纪90年代，埃克曼扩展了他的基本情绪列表，包括一系列积极和消极的情绪，这些情绪并非都对应于面部肌肉。新增的情绪是:
<nowiki>#</nowiki> 娱乐
<nowiki>#</nowiki> 娱乐
<nowiki>#</nowiki> 内疚
<nowiki>#</nowiki> 内疚
<nowiki>#</nowiki> 成就骄傲
<nowiki>#</nowiki> 成就感
<nowiki>#</nowiki> 解脱
<nowiki>#</nowiki> 解脱
<nowiki>#</nowiki> 满足
<nowiki>#</nowiki> 满足
<nowiki>#</nowiki> 感官愉悦
<nowiki>#</nowiki> 愉悦
<nowiki>#</nowiki> 羞耻
<nowiki>#</nowiki> 羞耻
====Facial Action Coding System====
====Facial Action Coding System====
==== 面部行为编码系统 ====
{{Main|Facial Action Coding System}}
{{Main|Facial Action Coding System}}
They are, basically, a contraction or a relaxation of one or more muscles. Psychologists have proposed the following classification of six basic emotions, according to their action units ("+" here mean "and"):
They are, basically, a contraction or a relaxation of one or more muscles. Psychologists have proposed the following classification of six basic emotions, according to their action units ("+" here mean "and"):
心理学家已经构想出一个系统，用来正式分类脸上情绪的物理表达。面部动作编码系统 (FACS) 的中心概念是由 Paul Ekman 和 Wallace V. Friesen 在 1978 年基于 Carl-Herman Hjortsjö 【31】的早期工作创建的，是动作单位 (AU)。它们基本上是一块或多块肌肉的收缩或放松。心理学家根据他们的行为单位，提出了以下六种基本情绪的分类(这里的“ +”是指“和”) :
心理学家已经构想出一个系统，用来正式分类脸上情绪的物理表达。面部动作编码系统 (FACS) 的中心概念是由保罗·埃克曼（ Paul Ekman ）和 华莱士·V·弗里森（Wallace V. Friesen） 在 1978 年基于 Carl-Herman Hjortsjö 【31】的早期工作创建的，动作单位 (Action unit, AU)是核心概念。它们基本上是一块或多块肌肉的收缩或放松。心理学家根据他们的行为单位，提出了以下六种基本情绪的分类(这里的“ +”是指“和”) :
{| class="wikitable sortable"
{| class="wikitable sortable"
{| class="wikitable sortable"
{| class="wikitable sortable"
|-
|-
! Emotion !! Action units
! 情感 !! 行为单位
|-
|-
| Happiness ||6+12
| 快乐 ||6+12
|-
|-
| Sadness || 1+4+15
| 悲伤 || 1+4+15
|-
|-
| Surprise || 1+2+5B+26
| 惊喜 || 1+2+5B+26
|-
|-
| Fear || 1+2+4+5+20+26
| 恐惧 || 1+2+4+5+20+26
|-
|-
| Anger || 4+5+7+23
| 愤怒 || 4+5+7+23
|-
|-
| Disgust || 9+15+16
| 厌恶 || 9+15+16
|-
|-
| Contempt || R12A+R14A
| 蔑视 || R12A+R14A
|}
|}
====Challenges in facial detection====
====Challenges in facial detection====
==== 面部情感检测的挑战 ====
As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost every kind of AI-based detection (speech recognition, face recognition, affect recognition), the accuracy of modeling and tracking has been an issue. As hardware evolves, as more data are collected and as new discoveries are made and new practices introduced, this lack of accuracy fades, leaving behind noise issues. However, methods for noise removal exist including neighborhood averaging, linear Gaussian smoothing, median filtering, or newer methods such as the Bacterial Foraging Optimization Algorithm.Clever Algorithms. "Bacterial Foraging Optimization Algorithm – Swarm Algorithms – Clever Algorithms" . Clever Algorithms. Retrieved 21 March 2011."Soft Computing". Soft Computing. Retrieved 18 March 2011.
As with every computational practice, in affect detection by facial processing, some obstacles need to be surpassed, in order to fully unlock the hidden potential of the overall algorithm or method employed. In the early days of almost every kind of AI-based detection (speech recognition, face recognition, affect recognition), the accuracy of modeling and tracking has been an issue. As hardware evolves, as more data are collected and as new discoveries are made and new practices introduced, this lack of accuracy fades, leaving behind noise issues. However, methods for noise removal exist including neighborhood averaging, linear Gaussian smoothing, median filtering, or newer methods such as the Bacterial Foraging Optimization Algorithm.Clever Algorithms. "Bacterial Foraging Optimization Algorithm – Swarm Algorithms – Clever Algorithms" . Clever Algorithms. Retrieved 21 March 2011."Soft Computing". Soft Computing. Retrieved 18 March 2011.
正如计算领域的多数问题一样，在面部情感检测研究中，也有很多障碍需要克服，以便充分释放算法和方法的全部潜力。在几乎所有基于人工智能的检测(语音识别、人脸识别、情感识别)的早期，建模和跟踪的准确性一直是个问题。随着硬件的发展，数据集的完善，新的发现和新的实践的引入，准确性问题逐渐被解决，留下了噪音问题。现有的去噪方法包括邻域平均法、线性高斯平滑法、中值滤波法【32】，或者更新的方法如菌群优化算法【33】【34】。
Other challenges include
Other challenges include
* Accuracy of recognition is improved by adding context; however, adding context and other modalities increases computational cost and complexity
* Accuracy of recognition is improved by adding context; however, adding context and other modalities increases computational cost and complexity
其他问题：
* 事实上，摆出的表情，正如大多数研究对象所使用的，是不自然的，因此训练这些算法可能不适用于自然表情。缺乏旋转运动的自由度。 正面使用时效果检测效果很好，但在将头部旋转 20 度以上时，“就出现了问题”【35】。面部表情并不总是与与之匹配的潜在情绪相对应（例如，它们可以摆姿势或伪装，或者一个人可以感受到情绪但保持“扑克脸”）。FACS 不包括动态，而动态可以帮助消除歧义（例如，真正快乐的微笑往往与“尝试看起来快乐”的微笑具有不同的动态）。FACS 组合与心理学家最初提出的情绪并不以 1:1 的方式对应（请注意，这种缺乏 1:1 映射的情况也发生在具有同音异义词和许多其他歧义来源的语音识别中，并且可能是通过引入其他信息渠道来缓解）。通过添加上下文提高了识别的准确性; 然而，添加上下文和其他模式增加了计算成本和复杂性
* 事实上，大多数研究所使用的摆拍表情是不自然的，因此训练这些算法可能不适用于自然表情。
* 缺乏旋转运动的自由度。正面使用时效果检测效果很好，但在将头部旋转 20 度以上时，就会出现问题【35】。
* 面部表情并不总是与对应的情绪相对应（例如，它们可以摆拍或伪装，或者保持“扑克脸”）。
* FACS 不包括动态，而动态可以帮助消除歧义（例如，真正快乐的微笑往往与“尝试看起来快乐”的微笑具有不同的动态）。
* FACS 组合与心理学家最初提出的情绪并不是一一对应的（这种缺乏 1:1 映射的情况也发生在具有同音异义词和许多其他歧义来源的语音识别中，可能通过引入其他信息渠道来缓解）。
* 通过添加上下文提高了识别的准确性; 然而，添加上下文和其他模式增加了计算成本和复杂性
===Body gesture===
===Body gesture===
=== 身体姿势 ===
{{Main|Gesture recognition}}
{{Main|Gesture recognition}}
Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders when you don't know the answer to a question, or they could be complex and meaningful as when communicating with sign language. Without making use of any object or surrounding environment, we can wave our hands, clap or beckon. On the other hand, when using objects, we can point at them, move, touch or handle these. A computer should be able to recognize these, analyze the context and respond in a meaningful way, in order to be efficiently used for Human–Computer Interaction.
Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders when you don't know the answer to a question, or they could be complex and meaningful as when communicating with sign language. Without making use of any object or surrounding environment, we can wave our hands, clap or beckon. On the other hand, when using objects, we can point at them, move, touch or handle these. A computer should be able to recognize these, analyze the context and respond in a meaningful way, in order to be efficiently used for Human–Computer Interaction.
Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders when you don't know the answer to a question, or they could be complex and meaningful as when communicating with sign language. Without making use of any object or surrounding environment, we can wave our hands, clap or beckon. On the other hand, when using objects, we can point at them, move, touch or handle these. A computer should be able to recognize these, analyze the context and respond in a meaningful way, in order to be efficiently used for Human–Computer Interaction.
Gestures could be efficiently used as a means of detecting a particular emotional state of the user, especially when used in conjunction with speech and face recognition. Depending on the specific action, gestures could be simple reflexive responses, like lifting your shoulders when you don't know the answer to a question, or they could be complex and meaningful as when communicating with sign language. Without making use of any object or surrounding environment, we can wave our hands, clap or beckon. On the other hand, when using objects, we can point at them, move, touch or handle these. A computer should be able to recognize these, analyze the context and respond in a meaningful way, in order to be efficiently used for Human–Computer Interaction.
身体姿态可以有效地检测用户特定的情绪状态，特别是与语音和面部识别结合使用时。根据具体的动作，姿态可以是简单的反射性反应，比如当你不知道一个问题的答案时抬起你的肩膀；或者它们可以是复杂和有意义的，比如当用手语交流时。不需要利用任何物体或周围环境，我们可以挥手、拍手或招手。另一方面，当我们借助外物时，可以指向，移动，触摸或者持握。计算机应该能够识别这些姿态，分析情景并作出响应，以便有效地用于人机交互。
There are many proposed methods<ref name="JK">J. K. Aggarwal, Q. Cai, Human Motion Analysis: A Review, Computer Vision and Image Understanding, Vol. 73, No. 3, 1999</ref> to detect the body gesture. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based.<ref name="Vladimir">{{cite journal | first1 = Vladimir I. | last1 = Pavlovic | first2 = Rajeev | last2 = Sharma | first3 = Thomas S. | last3 = Huang | url = http://www.cs.rutgers.edu/~vladimir/pub/pavlovic97pami.pdf | title = Visual Interpretation of Hand Gestures for Human–Computer Interaction: A Review | journal = [[IEEE Transactions on Pattern Analysis and Machine Intelligence]] | volume = 19 | issue = 7 | pages = 677–695 | year = 1997 | doi = 10.1109/34.598226 }}</ref> The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. On the other hand, appearance-based systems use images or videos to for direct interpretation. Hand gestures have been a common focus of body gesture detection methods.<ref name="Vladimir" />
There are many proposed methods<ref name="JK">J. K. Aggarwal, Q. Cai, Human Motion Analysis: A Review, Computer Vision and Image Understanding, Vol. 73, No. 3, 1999</ref> to detect the body gesture. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based.<ref name="Vladimir">{{cite journal | first1 = Vladimir I. | last1 = Pavlovic | first2 = Rajeev | last2 = Sharma | first3 = Thomas S. | last3 = Huang | url = http://www.cs.rutgers.edu/~vladimir/pub/pavlovic97pami.pdf | title = Visual Interpretation of Hand Gestures for Human–Computer Interaction: A Review | journal = [[IEEE Transactions on Pattern Analysis and Machine Intelligence]] | volume = 19 | issue = 7 | pages = 677–695 | year = 1997 | doi = 10.1109/34.598226 }}</ref> The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. On the other hand, appearance-based systems use images or videos to for direct interpretation. Hand gestures have been a common focus of body gesture detection methods.<ref name="Vladimir" />
There are many proposed methodsJ. K. Aggarwal, Q. Cai, Human Motion Analysis: A Review, Computer Vision and Image Understanding, Vol. 73, No. 3, 1999 to detect the body gesture. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based. The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. On the other hand, appearance-based systems use images or videos to for direct interpretation. Hand gestures have been a common focus of body gesture detection methods.
There are many proposed methodsJ. K. Aggarwal, Q. Cai, Human Motion Analysis: A Review, Computer Vision and Image Understanding, Vol. 73, No. 3, 1999 to detect the body gesture. Some literature differentiates 2 different approaches in gesture recognition: a 3D model based and an appearance-based. The foremost method makes use of 3D information of key elements of the body parts in order to obtain several important parameters, like palm position or joint angles. On the other hand, appearance-based systems use images or videos to for direct interpretation. Hand gestures have been a common focus of body gesture detection methods.
身体姿态检测已经提出了许多方法【36】。 一些文献提出了姿势识别的两种不同方法：基于 3D 模型和基于外观【37】。最重要的方法是利用人体关键部位的三维信息，获得手掌位置、关节角度等重要参数。另一方面，基于外观的系统直接使用图像或视频进行解释。手势一直是身体姿态检测方法的共同焦点【37】。
===Physiological monitoring===
===Physiological monitoring===
=== 生理检测 ===
This could be used to detect a user's affective state by monitoring and analyzing their physiological signs. These signs range from changes in heart rate and skin conductance to minute contractions of the facial muscles and changes in facial blood flow. This area is gaining momentum and we are now seeing real products that implement the techniques. The four main physiological signs that are usually analyzed are [[Pulse|blood volume pulse]], [[Skin conductance|galvanic skin response]], [[facial electromyography]], and facial color patterns.
This could be used to detect a user's affective state by monitoring and analyzing their physiological signs. These signs range from changes in heart rate and skin conductance to minute contractions of the facial muscles and changes in facial blood flow. This area is gaining momentum and we are now seeing real products that implement the techniques. The four main physiological signs that are usually analyzed are [[Pulse|blood volume pulse]], [[Skin conductance|galvanic skin response]], [[facial electromyography]], and facial color patterns.
This could be used to detect a user's affective state by monitoring and analyzing their physiological signs. These signs range from changes in heart rate and skin conductance to minute contractions of the facial muscles and changes in facial blood flow. This area is gaining momentum and we are now seeing real products that implement the techniques. The four main physiological signs that are usually analyzed are blood volume pulse, galvanic skin response, facial electromyography, and facial color patterns.
This could be used to detect a user's affective state by monitoring and analyzing their physiological signs. These signs range from changes in heart rate and skin conductance to minute contractions of the facial muscles and changes in facial blood flow. This area is gaining momentum and we are now seeing real products that implement the techniques. The four main physiological signs that are usually analyzed are blood volume pulse, galvanic skin response, facial electromyography, and facial color patterns.
生理信号可用于检测和分析情绪状态。这些生理信号通常包括脉搏、心率、面部肌肉每分钟收缩频率等。这个领域的发展势头越来越强劲，并且已经有了应用这些技术的实际产品。通常被分析的4个主要生理特征是血容量脉冲、皮肤电反应、面部肌电图和面部颜色。
==== Blood volume pulse ====
==== 血容量脉冲 ====
==== Overview ====
=====概述=====
=====Overview 概述=====
A subject's blood volume pulse (BVP) can be measured by a process called photoplethysmography, which produces a graph indicating blood flow through the extremities.<ref name="Picard, Rosalind 1998">Picard, Rosalind (1998). Affective Computing. MIT.</ref> The peaks of the waves indicate a cardiac cycle where the heart has pumped blood to the extremities. If the subject experiences fear or is startled, their heart usually 'jumps' and beats quickly for some time, causing the amplitude of the cardiac cycle to increase. This can clearly be seen on a photoplethysmograph when the distance between the trough and the peak of the wave has decreased. As the subject calms down, and as the body's inner core expands, allowing more blood to flow back to the extremities, the cycle will return to normal.
A subject's blood volume pulse (BVP) can be measured by a process called photoplethysmography, which produces a graph indicating blood flow through the extremities.<ref name="Picard, Rosalind 1998">Picard, Rosalind (1998). Affective Computing. MIT.</ref> The peaks of the waves indicate a cardiac cycle where the heart has pumped blood to the extremities. If the subject experiences fear or is startled, their heart usually 'jumps' and beats quickly for some time, causing the amplitude of the cardiac cycle to increase. This can clearly be seen on a photoplethysmograph when the distance between the trough and the peak of the wave has decreased. As the subject calms down, and as the body's inner core expands, allowing more blood to flow back to the extremities, the cycle will return to normal.
A subject's blood volume pulse (BVP) can be measured by a process called photoplethysmography, which produces a graph indicating blood flow through the extremities.Picard, Rosalind (1998). Affective Computing. MIT. The peaks of the waves indicate a cardiac cycle where the heart has pumped blood to the extremities. If the subject experiences fear or is startled, their heart usually 'jumps' and beats quickly for some time, causing the amplitude of the cardiac cycle to increase. This can clearly be seen on a photoplethysmograph when the distance between the trough and the peak of the wave has decreased. As the subject calms down, and as the body's inner core expands, allowing more blood to flow back to the extremities, the cycle will return to normal.
A subject's blood volume pulse (BVP) can be measured by a process called photoplethysmography, which produces a graph indicating blood flow through the extremities.Picard, Rosalind (1998). Affective Computing. MIT. The peaks of the waves indicate a cardiac cycle where the heart has pumped blood to the extremities. If the subject experiences fear or is startled, their heart usually 'jumps' and beats quickly for some time, causing the amplitude of the cardiac cycle to increase. This can clearly be seen on a photoplethysmograph when the distance between the trough and the peak of the wave has decreased. As the subject calms down, and as the body's inner core expands, allowing more blood to flow back to the extremities, the cycle will return to normal.
血容量脉搏(BVP)可以通过一个叫做光电容积扫描法的技术来测量，该方法产生一个图表来显示通过四肢的血液流动【38】。记录峰值代表着心搏周期中血流被泵到肢体末端。当被试受到惊吓或感到害怕时，他们往往会心跳加速，导致心率加快，从而在光电容积描记图上可以清楚地看到波峰与波谷间的距离变小。被试平静下来后，血液流回末端，心率回归正常。
=====Methodology =====
=====方法=====
=====Methodology 方法论=====
Infra-red light is shone on the skin by special sensor hardware, and the amount of light reflected is measured. The amount of reflected and transmitted light correlates to the BVP as light is absorbed by hemoglobin which is found richly in the bloodstream.
Infra-red light is shone on the skin by special sensor hardware, and the amount of light reflected is measured. The amount of reflected and transmitted light correlates to the BVP as light is absorbed by hemoglobin which is found richly in the bloodstream.
Infra-red light is shone on the skin by special sensor hardware, and the amount of light reflected is measured. The amount of reflected and transmitted light correlates to the BVP as light is absorbed by hemoglobin which is found richly in the bloodstream.
Infra-red light is shone on the skin by special sensor hardware, and the amount of light reflected is measured. The amount of reflected and transmitted light correlates to the BVP as light is absorbed by hemoglobin which is found richly in the bloodstream.
红外光通过特殊的传感器硬件照射在皮肤上，测量皮肤反射的光量。因为光线被血液中的血红蛋白吸收，所以反射光的数量与 BVP 相关。
=====Disadvantages =====
=====劣势=====
=====Disadvantages 劣势=====
It can be cumbersome to ensure that the sensor shining an infra-red light and monitoring the reflected light is always pointing at the same extremity, especially seeing as subjects often stretch and readjust their position while using a computer.
It can be cumbersome to ensure that the sensor shining an infra-red light and monitoring the reflected light is always pointing at the same extremity, especially seeing as subjects often stretch and readjust their position while using a computer.
There are other factors that can affect one's blood volume pulse. As it is a measure of blood flow through the extremities, if the subject feels hot, or particularly cold, then their body may allow more, or less, blood to flow to the extremities, all of this regardless of the subject's emotional state.
There are other factors that can affect one's blood volume pulse. As it is a measure of blood flow through the extremities, if the subject feels hot, or particularly cold, then their body may allow more, or less, blood to flow to the extremities, all of this regardless of the subject's emotional state.
确保发出红外光并监测反射光的传感器始终指向同一个末端可能很麻烦，尤其是观察对象经常伸展并重新调整其位置时。 还有其他因素会影响血容量脉冲，因为它是对通过四肢的血流量的量度，如果受试者感觉热，或特别冷，那么他们的身体可能允许更多或更少的血液流向四肢，所有这一切都与受试者的情绪状态无关。
[[File:Em-face-2.png|thumb|left| The corrugator supercilii muscle and zygomaticus major muscle are the 2 main muscles used for measuring the electrical activity, in facial electromyography|链接=Special:FilePath/Em-face-2.png]]
[[File:Em-face-2.png|thumb|left| The corrugator supercilii muscle and zygomaticus major muscle are the 2 main muscles used for measuring the electrical activity, in facial electromyography|链接=Special:FilePath/Em-face-2.png]]
====Facial electromyography====
====Facial electromyography====
==== 面部肌电图 ====
{{Main|Facial electromyography}}
{{Main|Facial electromyography}}
The corrugator supercilii muscle, also known as the 'frowning' muscle, draws the brow down into a frown, and therefore is the best test for negative, unpleasant emotional response.↵The zygomaticus major muscle is responsible for pulling the corners of the mouth back when you smile, and therefore is the muscle used to test for a positive emotional response.
The corrugator supercilii muscle, also known as the 'frowning' muscle, draws the brow down into a frown, and therefore is the best test for negative, unpleasant emotional response.↵The zygomaticus major muscle is responsible for pulling the corners of the mouth back when you smile, and therefore is the muscle used to test for a positive emotional response.
面部肌电图是一种通过放大肌肉纤维收缩时产生的微小电脉冲来测量面部肌肉电活动的技术【39】。面部表达大量情绪，然而，有两个主要的面部肌肉群通常被研究来检测情绪： 皱眉肌，也称为“皱眉”肌肉，将眉毛向下拉成皱眉，因此是对消极的、不愉快的情绪反应的最好测试。当微笑时，颧大肌负责将嘴角向后拉，因此是用于测试积极情绪反应的肌肉。
面部肌电图是一种通过放大肌肉纤维收缩时产生的微小电脉冲来测量面部肌肉电活动的技术【39】。面部表达大量情绪，然而，有两个主要的面部肌肉群通常被研究来检测情绪： 皱眉肌和颧大肌。皱眉肌将眉毛向下拉成皱眉，因此是对消极的、不愉快的情绪反应的最好反映。当微笑时，颧大肌负责将嘴角向后拉，因此是用于测试积极情绪反应的肌肉。
[[File:Gsrplot.svg|500px|thumb|Here we can see a plot of skin resistance measured using GSR and time whilst the subject played a video game. There are several peaks that are clear in the graph, which suggests that GSR is a good method of differentiating between an aroused and a non-aroused state. For example, at the start of the game where there is usually not much exciting game play, there is a high level of resistance recorded, which suggests a low level of conductivity and therefore less arousal. This is in clear contrast with the sudden trough where the player is killed as one is usually very stressed and tense as their character is killed in the game|链接=Special:FilePath/Gsrplot.svg]]
[[File:Gsrplot.svg|500px|thumb|Here we can see a plot of skin resistance measured using GSR and time whilst the subject played a video game. There are several peaks that are clear in the graph, which suggests that GSR is a good method of differentiating between an aroused and a non-aroused state. For example, at the start of the game where there is usually not much exciting game play, there is a high level of resistance recorded, which suggests a low level of conductivity and therefore less arousal. This is in clear contrast with the sudden trough where the player is killed as one is usually very stressed and tense as their character is killed in the game|链接=Special:FilePath/Gsrplot.svg]]
====Galvanic skin response====
====Galvanic skin response====
==== 皮肤电反应 ====
{{Main|Galvanic skin response}}
{{Main|Galvanic skin response}}
Galvanic skin response (GSR) is an outdated term for a more general phenomenon known as [Electrodermal Activity] or EDA. EDA is a general phenomena whereby the skin's electrical properties change. The skin is innervated by the [sympathetic nervous system], so measuring its resistance or conductance provides a way to quantify small changes in the sympathetic branch of the autonomic nervous system. As the sweat glands are activated, even before the skin feels sweaty, the level of the EDA can be captured (usually using conductance) and used to discern small changes in autonomic arousal. The more aroused a subject is, the greater the skin conductance tends to be.
Galvanic skin response (GSR) is an outdated term for a more general phenomenon known as [Electrodermal Activity] or EDA. EDA is a general phenomena whereby the skin's electrical properties change. The skin is innervated by the [sympathetic nervous system], so measuring its resistance or conductance provides a way to quantify small changes in the sympathetic branch of the autonomic nervous system. As the sweat glands are activated, even before the skin feels sweaty, the level of the EDA can be captured (usually using conductance) and used to discern small changes in autonomic arousal. The more aroused a subject is, the greater the skin conductance tends to be.
皮肤电反应(GSR)是一个过时的术语，更一般的现象称为[皮肤电活动]或 EDA。EDA 是皮肤电特性改变的普遍现象。皮肤受交感神经神经支配，因此测量皮肤的电阻或电导率可以量化自主神经系统交感神经分支的细微变化。当汗腺被激活时，甚至在皮肤出汗之前，EDA 的水平就可以被捕获(通常使用电导) ，并用于辨别自主神经唤醒的微小变化。一个主体越兴奋，皮肤导电反应就越强烈【38】。
皮肤电反应(Galvanic skin response，GSR)是一个过时的术语，更一般的现象称为[Electrodermal Activity，皮肤电活动]或 EDA。EDA 是皮肤电特性改变的普遍现象。皮肤受交感神经神经支配，因此测量皮肤的电阻或电导率可以量化自主神经系统交感神经分支的细微变化。当汗腺被激活时，甚至在皮肤出汗之前，EDA 的水平就可以被捕获(通常使用电导) ，并用于辨别自主神经唤醒的微小变化。一个主体越兴奋，皮肤导电反应就越强烈【38】。
Skin conductance is often measured using two small [[silver-silver chloride]] electrodes placed somewhere on the skin and applying a small voltage between them. To maximize comfort and reduce irritation the electrodes can be placed on the wrist, legs, or feet, which leaves the hands fully free for daily activity.
Skin conductance is often measured using two small [[silver-silver chloride]] electrodes placed somewhere on the skin and applying a small voltage between them. To maximize comfort and reduce irritation the electrodes can be placed on the wrist, legs, or feet, which leaves the hands fully free for daily activity.
皮肤导电反应通常是通过放置在皮肤某处的小型氯化银电极并在两者之间施加一个小电压来测量的。为了最大限度地舒适和减少刺激，电极可以放在手腕、腿上或脚上，这样手就可以完全自由地进行日常活动。
皮肤导电反应通常是通过放置在皮肤某处的小型氯化银电极并在两者之间施加一个小电压来测量的。为了最大限度地舒适和减少刺激，电极可以放在手腕、腿上或脚上，这样手就可以完全自由地进行日常活动。
====Facial color====
====Facial color====
==== 面部颜色 ====
=====Overview=====
=====Overview=====
===== 概述 =====
The surface of the human face is innervated with a large network of blood vessels. Blood flow variations in these vessels yield visible color changes on the face. Whether or not facial emotions activate facial muscles, variations in blood flow, blood pressure, glucose levels, and other changes occur. Also, the facial color signal is independent from that provided by facial muscle movements.Carlos F. Benitez-Quiroz, Ramprakash Srinivasan, Aleix M. Martinez, Facial color is an efficient mechanism to visually transmit emotion, PNAS. April 3, 2018 115 (14) 3581–3586; first published March 19, 2018 https://doi.org/10.1073/pnas.1716084115.
The surface of the human face is innervated with a large network of blood vessels. Blood flow variations in these vessels yield visible color changes on the face. Whether or not facial emotions activate facial muscles, variations in blood flow, blood pressure, glucose levels, and other changes occur. Also, the facial color signal is independent from that provided by facial muscle movements.Carlos F. Benitez-Quiroz, Ramprakash Srinivasan, Aleix M. Martinez, Facial color is an efficient mechanism to visually transmit emotion, PNAS. April 3, 2018 115 (14) 3581–3586; first published March 19, 2018 https://doi.org/10.1073/pnas.1716084115.
人脸表面由大量血管网络支配。 这些血管中的血流变化会在脸上产生可见的颜色变化。 无论面部情绪是否激活面部肌肉，都会发生血流量、血压、血糖水平和其他变化的变化。 此外，面部颜色信号与面部肌肉运动提供的信号无关【40】。
人脸表面由大量血管网络支配。 这些血管中的血流变化会在脸上产生可见的颜色变化。 无论面部情绪是否激活面部肌肉，都会发生血流量、血压、血糖水平和其他变化。 此外，面部颜色信号与面部肌肉运动提供的信号无关【40】。
=====Methodology=====
=====Methodology=====
===== 方法 =====
===Visual aesthetics===
===Visual aesthetics===
=== 视觉审美 ===
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities is a highly subjective task. Computer scientists at Penn State treat the challenge of automatically inferring the aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated on-line photo sharing website as a data source.<ref name="datta">Ritendra Datta, Dhiraj Joshi, Jia Li and James Z. Wang, [https://web.archive.org/web/20181030170421/https://pdfs.semanticscholar.org/8772/877ceb40d6d8685655145034740f3df7baad.pdf Studying Aesthetics in Photographic Images Using a Computational Approach], Lecture Notes in Computer Science, vol. 3953, Proceedings of the European Conference on Computer Vision, Part III, pp. 288–301, Graz, Austria, May 2006.</ref> They extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images.
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities is a highly subjective task. Computer scientists at Penn State treat the challenge of automatically inferring the aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated on-line photo sharing website as a data source.<ref name="datta">Ritendra Datta, Dhiraj Joshi, Jia Li and James Z. Wang, [https://web.archive.org/web/20181030170421/https://pdfs.semanticscholar.org/8772/877ceb40d6d8685655145034740f3df7baad.pdf Studying Aesthetics in Photographic Images Using a Computational Approach], Lecture Notes in Computer Science, vol. 3953, Proceedings of the European Conference on Computer Vision, Part III, pp. 288–301, Graz, Austria, May 2006.</ref> They extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images.
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities is a highly subjective task. Computer scientists at Penn State treat the challenge of automatically inferring the aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated on-line photo sharing website as a data source.Ritendra Datta, Dhiraj Joshi, Jia Li and James Z. Wang, Studying Aesthetics in Photographic Images Using a Computational Approach, Lecture Notes in Computer Science, vol. 3953, Proceedings of the European Conference on Computer Vision, Part III, pp. 288–301, Graz, Austria, May 2006. They extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images.
Aesthetics, in the world of art and photography, refers to the principles of the nature and appreciation of beauty. Judging beauty and other aesthetic qualities is a highly subjective task. Computer scientists at Penn State treat the challenge of automatically inferring the aesthetic quality of pictures using their visual content as a machine learning problem, with a peer-rated on-line photo sharing website as a data source.Ritendra Datta, Dhiraj Joshi, Jia Li and James Z. Wang, Studying Aesthetics in Photographic Images Using a Computational Approach, Lecture Notes in Computer Science, vol. 3953, Proceedings of the European Conference on Computer Vision, Part III, pp. 288–301, Graz, Austria, May 2006. They extract certain visual features based on the intuition that they can discriminate between aesthetically pleasing and displeasing images.
美学，在艺术和摄影界，是指美的本质和欣赏原则。 对美和其他审美特质的判断是一项高度主观的任务。 宾夕法尼亚州立大学的计算机科学家将自动评价图像的审美特质视作机器学习的一大挑战，他们将一个同行评级的在线照片分享网站作为数据源，从中抽取了特定的视觉特征，可以区分审美上的愉悦与否。
==Potential applications==
==Potential applications==
== 潜在应用 ==
===Education===
===Education===
=== 教育 ===
Affection influences learners' learning state. Using affective computing technology, computers can judge the learners' affection and learning state by recognizing their facial expressions. In education, the teacher can use the analysis result to understand the student's learning and accepting ability, and then formulate reasonable teaching plans. At the same time, they can pay attention to students' inner feelings, which is helpful to students' psychological health. Especially in distance education, due to the separation of time and space, there is no emotional incentive between teachers and students for two-way communication. Without the atmosphere brought by traditional classroom learning, students are easily bored, and affect the learning effect. Applying affective computing in distance education system can effectively improve this situation.
Affection influences learners' learning state. Using affective computing technology, computers can judge the learners' affection and learning state by recognizing their facial expressions. In education, the teacher can use the analysis result to understand the student's learning and accepting ability, and then formulate reasonable teaching plans. At the same time, they can pay attention to students' inner feelings, which is helpful to students' psychological health. Especially in distance education, due to the separation of time and space, there is no emotional incentive between teachers and students for two-way communication. Without the atmosphere brought by traditional classroom learning, students are easily bored, and affect the learning effect. Applying affective computing in distance education system can effectively improve this situation.
<ref>http://www.learntechlib.org/p/173785/</ref>
<ref>http://www.learntechlib.org/p/173785/</ref>
=== Healthcare ===
=== Healthcare ===
=== 医疗 ===
[[Social robot]]s, as well as a growing number of robots used in health care benefit from emotional awareness because they can better judge users' and patient's emotional states and alter their actions/programming appropriately. This is especially important in those countries with growing aging populations and/or a lack of younger workers to address their needs.<ref>{{Cite book|title=Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence|last=Yonck|first=Richard|publisher=Arcade Publishing|year=2017|isbn=9781628727333|location=New York|pages=150–153|oclc=956349457}}</ref>
[[Social robot]]s, as well as a growing number of robots used in health care benefit from emotional awareness because they can better judge users' and patient's emotional states and alter their actions/programming appropriately. This is especially important in those countries with growing aging populations and/or a lack of younger workers to address their needs.<ref>{{Cite book|title=Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence|last=Yonck|first=Richard|publisher=Arcade Publishing|year=2017|isbn=9781628727333|location=New York|pages=150–153|oclc=956349457}}</ref>
Social robots, as well as a growing number of robots used in health care benefit from emotional awareness because they can better judge users' and patient's emotional states and alter their actions/programming appropriately. This is especially important in those countries with growing aging populations and/or a lack of younger workers to address their needs.
Social robots, as well as a growing number of robots used in health care benefit from emotional awareness because they can better judge users' and patient's emotional states and alter their actions/programming appropriately. This is especially important in those countries with growing aging populations and/or a lack of younger workers to address their needs.
社会机器人，以及越来越多的机器人在医疗保健中的应用都受益于情感意识，因为它们可以更好地判断用户和病人的情感状态，并适当地改变他们的行为。在人口老龄化日益严重和缺乏年轻工人的国家，这一点尤为重要【45】。
Affective computing is also being applied to the development of communicative technologies for use by people with autism.<ref>[http://affect.media.mit.edu/projects.php Projects in Affective Computing]</ref> The affective component of a text is also increasingly gaining attention, particularly its role in the so-called emotional or [[emotive Internet]].<ref>Shanahan, James; Qu, Yan; Wiebe, Janyce (2006). ''Computing Attitude and Affect in Text: Theory and Applications''. Dordrecht: Springer Science & Business Media. p. 94. {{ISBN|1402040261}}</ref>
Affective computing is also being applied to the development of communicative technologies for use by people with autism.<ref>[http://affect.media.mit.edu/projects.php Projects in Affective Computing]</ref> The affective component of a text is also increasingly gaining attention, particularly its role in the so-called emotional or [[emotive Internet]].<ref>Shanahan, James; Qu, Yan; Wiebe, Janyce (2006). ''Computing Attitude and Affect in Text: Theory and Applications''. Dordrecht: Springer Science & Business Media. p. 94. {{ISBN|1402040261}}</ref>
情感计算也被应用于交流技术的发展，以供孤独症患者使用【46】。情感计算项目文本中的情感成分也越来越受到关注，特别是它在所谓的情感或情感互联网中的作用【47】。
情感计算也被应用于交流技术的发展，以供孤独症患者使用【46】。情感计算项目文本中的情感成分也越来越受到关注，特别是它在所谓的情感或情感互联网中的作用【47】。
===Video games===
=== 电子游戏 ===
===Video games===
Affective video games can access their players' emotional states through biofeedback devices. A particularly simple form of biofeedback is available through gamepads that measure the pressure with which a button is pressed: this has been shown to correlate strongly with the players' level of arousal; at the other end of the scale are brain–computer interfaces. Affective games have been used in medical research to support the emotional development of autistic children.
Affective video games can access their players' emotional states through biofeedback devices. A particularly simple form of biofeedback is available through gamepads that measure the pressure with which a button is pressed: this has been shown to correlate strongly with the players' level of arousal; at the other end of the scale are brain–computer interfaces. Affective games have been used in medical research to support the emotional development of autistic children.
情感型电子游戏可以通过生物反馈设备获取玩家的情绪状态【48】。有一些特别简单的生物反馈形式，如通过游戏手柄来测量按下按钮的压力，来获取玩家的唤醒度水平【49】； 另一方面是脑机接口【50】【51】。情感游戏已被用于医学研究，以改善自闭症儿童的情感发展【52】。
===Other applications===
===Other applications===
=== 其他应用 ===
Other potential applications are centered around social monitoring. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry.<ref>{{cite web|url=https://gizmodo.com/in-car-facial-recognition-detects-angry-drivers-to-prev-1543709793|title=In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage|date=30 August 2018|website=Gizmodo}}</ref> Affective computing has potential applications in [[human computer interaction|human–computer interaction]], such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.<ref>{{cite journal|last1=Janssen|first1=Joris H.|last2=van den Broek|first2=Egon L.|date=July 2012|title=Tune in to Your Emotions: A Robust Personalized Affective Music Player|journal=User Modeling and User-Adapted Interaction|volume=22|issue=3|pages=255–279|doi=10.1007/s11257-011-9107-7|doi-access=free}}</ref>
Other potential applications are centered around social monitoring. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry.<ref>{{cite web|url=https://gizmodo.com/in-car-facial-recognition-detects-angry-drivers-to-prev-1543709793|title=In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage|date=30 August 2018|website=Gizmodo}}</ref> Affective computing has potential applications in [[human computer interaction|human–computer interaction]], such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.<ref>{{cite journal|last1=Janssen|first1=Joris H.|last2=van den Broek|first2=Egon L.|date=July 2012|title=Tune in to Your Emotions: A Robust Personalized Affective Music Player|journal=User Modeling and User-Adapted Interaction|volume=22|issue=3|pages=255–279|doi=10.1007/s11257-011-9107-7|doi-access=free}}</ref>
Other potential applications are centered around social monitoring. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry. Affective computing has potential applications in human–computer interaction, such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.
Other potential applications are centered around social monitoring. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry. Affective computing has potential applications in human–computer interaction, such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.
其他潜在的应用主要围绕社会监控。例如，一辆汽车可以监控所有乘客的情绪，并采取额外的安全措施。如果发现司机生气，就向其他车辆发出警报【53】。情感计算在人机交互方面有着潜在的应用，比如情感镜子可以让用户看到自己的表现; 情感监控代理在发送愤怒邮件之前发送警告; 甚至音乐播放器可以根据情绪选择音轨【54】。
One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example).<ref>{{cite web|url=https://www.sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|title=Mona Lisa: Smiling? Computer Scientists Develop Software That Evaluates Facial Expressions|date=1 August 2006|website=ScienceDaily|archive-url=https://web.archive.org/web/20071019235625/http://sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|archive-date=19 October 2007|url-status=dead}}</ref> Companies would then be able to use such analysis to infer whether their product will or will not be well received by the respective market.
One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example).<ref>{{cite web|url=https://www.sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|title=Mona Lisa: Smiling? Computer Scientists Develop Software That Evaluates Facial Expressions|date=1 August 2006|website=ScienceDaily|archive-url=https://web.archive.org/web/20071019235625/http://sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|archive-date=19 October 2007|url-status=dead}}</ref> Companies would then be able to use such analysis to infer whether their product will or will not be well received by the respective market.
One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example). Companies would then be able to use such analysis to infer whether their product will or will not be well received by the respective market.
One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example). Companies would then be able to use such analysis to infer whether their product will or will not be well received by the respective market.
罗马尼亚研究人员尼库 · 塞贝博士在一次采访中提出的一个想法是，当一个人使用某种产品时，对他的脸进行分析(他提到了冰淇淋作为一个例子)【55】。然后，公司就能够利用这种分析来推断他们的产品是否会受到各自市场的欢迎。
罗马尼亚研究人员尼库 · 塞贝博士在一次采访中提出的一个想法是，当一个人使用某种产品时，对他的面部进行分析(他提到了冰淇淋作为一个例子)【55】，公司就能够利用这种分析来推断他们的产品是否会受到各自市场的欢迎。
One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.
One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.
One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.
One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.
人们也可以利用情感状态识别来判断电视广告的影响，通过实时录像和随后对人们面部表情的研究，之后对大量主题的结果进行平均，我们就能知道这个广告(或电影)是否达到了预期的效果，以及观众最感兴趣的元素是什么。
==Cognitivist vs. interactional approaches==
== 认知主义与交互方法之争 ==
==Cognitivist vs. interactional approaches==
Within the field of [[human–computer interaction]], Rosalind Picard's [[cognitivism (psychology)|cognitivist]] or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional" [[pragmatism|pragmatist]] approach taken by Kirsten Boehner and others which views emotion as inherently social.<ref>{{cite journal|last1=Battarbee|first1=Katja|last2=Koskinen|first2=Ilpo|title=Co-experience: user experience as interaction|journal=CoDesign|date=2005|volume=1|issue=1|pages=5–18|url=http://www2.uiah.fi/~ikoskine/recentpapers/mobile_multimedia/coexperience_reprint_lr_5-18.pdf|doi=10.1080/15710880412331289917|citeseerx=10.1.1.294.9178|s2cid=15296236}}</ref>
Within the field of [[human–computer interaction]], Rosalind Picard's [[cognitivism (psychology)|cognitivist]] or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional" [[pragmatism|pragmatist]] approach taken by Kirsten Boehner and others which views emotion as inherently social.<ref>{{cite journal|last1=Battarbee|first1=Katja|last2=Koskinen|first2=Ilpo|title=Co-experience: user experience as interaction|journal=CoDesign|date=2005|volume=1|issue=1|pages=5–18|url=http://www2.uiah.fi/~ikoskine/recentpapers/mobile_multimedia/coexperience_reprint_lr_5-18.pdf|doi=10.1080/15710880412331289917|citeseerx=10.1.1.294.9178|s2cid=15296236}}</ref>
Within the field of human–computer interaction, Rosalind Picard's cognitivist or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional" pragmatist approach taken by Kirsten Boehner and others which views emotion as inherently social.
Within the field of human–computer interaction, Rosalind Picard's cognitivist or "information model" concept of emotion has been criticized by and contrasted with the "post-cognitivist" or "interactional" pragmatist approach taken by Kirsten Boehner and others which views emotion as inherently social.
在人机交互领域，罗莎琳德 · 皮卡德的情绪认知主义或“信息模型”概念受到了后认知主义或“互动”实用主义者柯尔斯滕 · 博纳等人的批判和对比【56】。
在人机交互领域，罗莎琳德 · 皮卡德的情绪认知主义或“信息模型”概念受到了实用主义者柯尔斯滕 · 博纳等人的批判和对比，他们坚信“后认知主义”和“交互方法”【56】。
Picard's focus is human–computer interaction, and her goal for affective computing is to "give computers the ability to recognize, express, and in some cases, 'have' emotions".<ref name="Affective Computing" /> In contrast, the interactional approach seeks to help "people to understand and experience their own emotions"<ref name="How emotion is made and measured">{{cite journal|last1=Boehner|first1=Kirsten|last2=DePaula|first2=Rogerio|last3=Dourish|first3=Paul|last4=Sengers|first4=Phoebe|title=How emotion is made and measured|journal=International Journal of Human–Computer Studies|date=2007|volume=65|issue=4|pages=275–291|doi=10.1016/j.ijhcs.2006.11.016}}</ref> and to improve computer-mediated interpersonal communication. It does not necessarily seek to map emotion into an objective mathematical model for machine interpretation, but rather let humans make sense of each other's emotional expressions in open-ended ways that might be ambiguous, subjective, and sensitive to context.<ref name="How emotion is made and measured" />{{rp|284}}{{example needed|date=September 2018}}
Picard's focus is human–computer interaction, and her goal for affective computing is to "give computers the ability to recognize, express, and in some cases, 'have' emotions".<ref name="Affective Computing" /> In contrast, the interactional approach seeks to help "people to understand and experience their own emotions"<ref name="How emotion is made and measured">{{cite journal|last1=Boehner|first1=Kirsten|last2=DePaula|first2=Rogerio|last3=Dourish|first3=Paul|last4=Sengers|first4=Phoebe|title=How emotion is made and measured|journal=International Journal of Human–Computer Studies|date=2007|volume=65|issue=4|pages=275–291|doi=10.1016/j.ijhcs.2006.11.016}}</ref> and to improve computer-mediated interpersonal communication. It does not necessarily seek to map emotion into an objective mathematical model for machine interpretation, but rather let humans make sense of each other's emotional expressions in open-ended ways that might be ambiguous, subjective, and sensitive to context.<ref name="How emotion is made and measured" />{{rp|284}}{{example needed|date=September 2018}}
Picard's focus is human–computer interaction, and her goal for affective computing is to "give computers the ability to recognize, express, and in some cases, 'have' emotions". In contrast, the interactional approach seeks to help "people to understand and experience their own emotions" and to improve computer-mediated interpersonal communication. It does not necessarily seek to map emotion into an objective mathematical model for machine interpretation, but rather let humans make sense of each other's emotional expressions in open-ended ways that might be ambiguous, subjective, and sensitive to context.
Picard's focus is human–computer interaction, and her goal for affective computing is to "give computers the ability to recognize, express, and in some cases, 'have' emotions". In contrast, the interactional approach seeks to help "people to understand and experience their own emotions" and to improve computer-mediated interpersonal communication. It does not necessarily seek to map emotion into an objective mathematical model for machine interpretation, but rather let humans make sense of each other's emotional expressions in open-ended ways that might be ambiguous, subjective, and sensitive to context.
皮卡德的研究重点是人机交互，她研究情感计算的目标是“赋予计算机识别、表达、在某些情况下‘拥有’情感的能力”【4】。相比之下，交互式的方法旨在帮助“人们理解和体验他们自己的情绪”【57】，并改善以电脑为媒介的人际沟通。它认为不一定将情感映射到机器解释的客观数学模型中，重要的是让人类畅通无阻地理解彼此的情感，而这些情感信息往往会是歧义的、主观的或上下文敏感的【57】。
Picard's critics describe her concept of emotion as "objective, internal, private, and mechanistic". They say it reduces emotion to a discrete psychological signal occurring inside the body that can be measured and which is an input to cognition, undercutting the complexity of emotional experience.<ref name="How emotion is made and measured" />{{rp|280}}<ref name="How emotion is made and measured" />{{rp|278}}
Picard's critics describe her concept of emotion as "objective, internal, private, and mechanistic". They say it reduces emotion to a discrete psychological signal occurring inside the body that can be measured and which is an input to cognition, undercutting the complexity of emotional experience.<ref name="How emotion is made and measured" />{{rp|280}}<ref name="How emotion is made and measured" />{{rp|278}}
Picard's critics describe her concept of emotion as "objective, internal, private, and mechanistic". They say it reduces emotion to a discrete psychological signal occurring inside the body that can be measured and which is an input to cognition, undercutting the complexity of emotional experience.
Picard's critics describe her concept of emotion as "objective, internal, private, and mechanistic". They say it reduces emotion to a discrete psychological signal occurring inside the body that can be measured and which is an input to cognition, undercutting the complexity of emotional experience.
皮卡德的批评者将她的情感概念描述为“客观的、内在的、私人的和机械的”。他们认为她把情绪简化为发生在身体内部的一个离散的心理信号，这个信号可以被测量，并且是认知的输入，削弱了情绪体验的复杂性。
The interactional approach asserts that though emotion has biophysical aspects, it is "culturally grounded, dynamically experienced, and to some degree constructed in action and interaction".<ref name="How emotion is made and measured" />{{rp|276}} Put another way, it considers "emotion as a social and cultural product experienced through our interactions".<ref>{{cite journal|last1=Boehner|first1=Kirsten|last2=DePaula|first2=Rogerio|last3=Dourish|first3=Paul|last4=Sengers|first4=Phoebe|title=Affection: From Information to Interaction|journal=Proceedings of the Aarhus Decennial Conference on Critical Computing|date=2005|pages=59–68}}</ref><ref name="How emotion is made and measured" /><ref>{{cite journal|last1=Hook|first1=Kristina|last2=Staahl|first2=Anna|last3=Sundstrom|first3=Petra|last4=Laaksolahti|first4=Jarmo|title=Interactional empowerment|journal=Proc. CHI|date=2008|pages=647–656|url=http://research.microsoft.com/en-us/um/cambridge/projects/hci2020/pdf/interactional%20empowerment%20final%20Jan%2008.pdf}}</ref>
The interactional approach asserts that though emotion has biophysical aspects, it is "culturally grounded, dynamically experienced, and to some degree constructed in action and interaction".<ref name="How emotion is made and measured" />{{rp|276}} Put another way, it considers "emotion as a social and cultural product experienced through our interactions".<ref>{{cite journal|last1=Boehner|first1=Kirsten|last2=DePaula|first2=Rogerio|last3=Dourish|first3=Paul|last4=Sengers|first4=Phoebe|title=Affection: From Information to Interaction|journal=Proceedings of the Aarhus Decennial Conference on Critical Computing|date=2005|pages=59–68}}</ref><ref name="How emotion is made and measured" /><ref>{{cite journal|last1=Hook|first1=Kristina|last2=Staahl|first2=Anna|last3=Sundstrom|first3=Petra|last4=Laaksolahti|first4=Jarmo|title=Interactional empowerment|journal=Proc. CHI|date=2008|pages=647–656|url=http://research.microsoft.com/en-us/um/cambridge/projects/hci2020/pdf/interactional%20empowerment%20final%20Jan%2008.pdf}}</ref>
The interactional approach asserts that though emotion has biophysical aspects, it is "culturally grounded, dynamically experienced, and to some degree constructed in action and interaction". Put another way, it considers "emotion as a social and cultural product experienced through our interactions".
The interactional approach asserts that though emotion has biophysical aspects, it is "culturally grounded, dynamically experienced, and to some degree constructed in action and interaction". Put another way, it considers "emotion as a social and cultural product experienced through our interactions".
交互方法断言，虽然情绪具有生物物理性，但它是“以文化为基础的，动态体验的，并在某种程度上构建于行动和互动中”【57】。换句话说，它认为“情感是一种通过我们的互动体验到的社会和文化产物”【57】【58】【59】。
==See also==
==See also==
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