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第271行： − 面部表情的检测和处理通过光流、隐马尔可夫模型、神经网络处理或主动外观模型等多种方法实现。多模式识别可以组合或融合多种模式。面部表情和语音韵律，面部表情和手势，或面部表情与语音和文本的多模态数据和元数据分析) ，以提供一个更稳健的估计主题的情绪状态。Affectiva 是一家与情感计算直接相关的公司(由 Rosalind Picard 和 Rana El Kaliouby 共同创办) ，旨在研究面部情感检测的解决方案和软件。+ 第278行：
第280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出的面部表情。在提出的表达式数据库中，要求参与者显示不同的基本情感表达，而在自发表达式数据库中，表达是自然的。自发的情绪诱导需要在选择合适的刺激物时付出巨大的努力，这会导致丰富的预期情绪的展示。其次，这个过程包括由训练有素的人手动标记情绪，使数据库高度可靠。由于对表达式及其强度的感知在本质上是主观的，专家的注释对于验证是必不可少的。+ 第284行：
第286行： − 研究人员使用三种类型的数据库进行研究，比如只使用峰值表情图像的数据库，描绘从中性到峰值的情感的图像序列数据库，以及带有情感注释的视频剪辑。面部表情数据库是面部表情识别领域的一个重要研究课题。两个广泛使用的数据库是 CK + 和 JAFFE。+ 第295行：
第297行： − 20世纪60年代末，Paul Ekman 在福尔部落做了跨文化研究，提出面部表情不是由文化决定的，而是普遍存在的巴布亚新几内亚。因此，他认为它们是生物起源的，因此可以安全而正确地归类。因此，他在1972年正式提出了六种基本情感:+ 第345行：
第347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: # 娱乐 # 轻蔑 # 满足 # 尴尬 # 兴奋 # 内疚 # 成就骄傲 # 解脱 # 满足 # 感官愉悦 # 羞耻+ + + + + + + + + + + + + + + + + + + + + + + 第356行：
第380行： − 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。1978年，Paul Ekman 和 Wallace v. Friesen 根据 Carl-Herman hjortsjö 的早期作品《面部动作编码系统面部动作编码系统和 FACS 手册》创建了 FACS 的中心概念。一张人类的脸。检索2011年3月21日. 是行动单位(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”) :+ 第405行：
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第447行： − 其他的挑战包括 + − + − * 缺乏旋转运动自由。情感检测在正面使用时非常有效，但当头部旋转20度以上时，“就会出现问题”。威廉姆斯，马克。「更佳面部辨认软件-技术检讨」。技术评论: 关于技术未来的权威。2011年3月21日。 − * 面部表情并不总是对应与其相匹配的潜在情绪(例如:。他们可以摆姿势或者伪装，或者一个人可以感受情绪但是保持一张扑克脸)。 − * FACS 不包括动态，而动态可以帮助消除歧义(例如:。真正幸福的微笑往往比“试着看起来幸福”的微笑有着不同的动力。) − * FACS 的组合与心理学家最初提出的情绪并不是1:1的对应关系(请注意，这种缺乏1:1映射的情况在同音字和同音字以及许多其他歧义来源的语音识别中也会出现，并且可以通过引入其他信息渠道来减轻这种情绪)。 − * 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 第438行：
第457行： − 手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，手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，比如当与手语交流时。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444行：
第463行： − 提出了许多方法。人体运动分析: 评论，计算机视觉与图像理解，第卷。73，No.1999年3月3日，用来探测身体姿势。一些文献将手势识别的2种不同方法进行了区分: 基于3 d 模型和基于外观的。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。+ 第451行：
第470行： − 这可以用来检测用户的情感状态，通过监测和分析他们的生理信号。这些信号包括心率和皮肤导电反应的变化，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。+ 第469行：
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第514行： − 要确保传感器发出红外光并监测反射光总是指向同一极端，可能有些麻烦，尤其是在使用计算机时，受试者经常伸展和重新调整自己的位置。还有其他因素可以影响一个人的血容量脉搏。因为这是通过四肢血液流量的测量，如果受试者感到热或特别冷，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。+ − − thumb|left| The corrugator supercilii muscle and zygomaticus major muscle are the 2 main muscles used for measuring the electrical activity, in facial electromyography − − 皱眉肌和颧肌是用来测量面部肌电图电活动的主要肌肉 第514行：
第529行： − 面部肌电图是一种通过放大肌肉收缩时产生的微小电脉冲来测量面部肌肉电活动的技术。O </o < o </o < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o </o < > < o < > < o </o < > < o </o < > < o </o < > < o < > < o </o < > < o < > < o < > < o </o < > < o </o < > < o < > < o < > < o < > < o </o < > < o </o < > < o </o < > < o < > < > < o < > < > < o < > < > < > < o < > < > < o </o < >.当你微笑时，颧肌的主要肌肉负责将嘴角向后拉，因此是用来测试积极情绪反应的肌肉。+ − − 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 − − 500px | thumb | Here we can see a plot of skin resistance measured using GSR and time while the subject played a video game.在图中有几个明显的峰值，这表明 GSR 是区分性唤起和非性唤起状态的一个很好的方法。例如，在游戏开始的时候，通常没有多少激动人心的游戏，但是有一个高水平的电阻记录，这意味着低水平的电导率，因此唤起较少。这与玩家被杀的突然低谷形成了鲜明的对比，因为他们的角色在游戏中被杀时通常非常紧张和紧张 第529行：
第540行： − + 第553行：
第564行： − 人脸的表面受到大型血管网的支配。这些血管中的血流变化在脸部产生可见的颜色变化。无论面部情绪是否激活面部肌肉，血流量、血压、血糖水平和其他变化都会发生。此外，面部颜色信号独立于面部肌肉运动提供的信号。卡洛斯 · f · 贝尼特斯-奎罗斯，兰帕卡什 · 斯里尼瓦桑，阿莱克斯 · m · 马丁内斯，面部颜色是一种有效的机制，可以在视觉上传达情感，PNAS。2018年4月3日115(14)3581-3586; 首次发表于2018年3月19日 https://doi.org/10.1073/pnas.1716084115。+ 第565行：
第576行： − 方法是基于面部颜色的变化。德劳内三角化被用来创建三角形的局部区域。其中一些定义嘴和眼睛内部的三角形(巩膜和虹膜)被移除。使用左边三角形区域的像素来创建特征向量。它表明将标准 RGB 颜色空间的像素颜色转换为颜色空间，如 oRGB 颜色空间。和 p. Shirley，org b: 一个实用的对手色彩空间，计算机图形学，IEEE 计算机图形学与应用，29(1) : 42-55,2009。或 LMS 通道在处理面孔时表现更好。使用面部颜色和深度学习方法的微表情分类，IEEE 国际计算机视觉会议(ICCV) ，2019，pp。0–0.因此，将上面的矢量映射到较好的颜色空间，并分解为红绿色和黄蓝色通道。然后使用深度学习的方法来找到等效的情绪。+ 第572行：
第583行： − 美学，在艺术和摄影的世界里，是指自然的原则和审美的原则。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利用图片的视觉内容自动推断图片的审美质量的挑战视为一个机器学习问题，同行评级的在线照片共享网站则是一个数据源。利 · 达塔，Dhiraj Joshi，贾力和詹姆斯 · 王，用计算方法研究摄影图像美学，计算机科学讲义，第卷。3953，Proceedings of the European Conference on Computer Vision，Part III，pp.288-301，格拉茨，2006年5月。他们提取某些视觉特征是基于这样一种直觉，即他们可以区分美观的和不美观的图像。+ 第582行：
第593行： − 情感影响学习者的学习状态。利用情感计算技术，计算机可以通过学习者的面部表情识别来判断学习者的情感和学习状态。在教学中，教师可以利用分析结果了解学生的学习和接受能力，制定合理的教学计划。同时关注学生的内心感受，有利于学生的心理健康。特别是在远程教育中，由于时间和空间的分离，师生之间缺乏双向交流的情感激励。没有了传统课堂学习带来的氛围，学生很容易感到无聊，影响学习效果。将情感计算应用于远程教育系统可以有效地改善这种状况。http://www.learntechlib.org/p/173785/+ 第589行：
第600行： − + 第595行：
第606行： − 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。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。夏纳汉，詹姆斯; 曲，严; 韦伯，詹尼策(2006)。文本中的计算态度和情感: 理论与应用。多德雷赫特: 斯普林格科学与商业媒体。3月94日。+ 第616行：
第627行： − 情感视频游戏可以通过生物反馈设备进入玩家的情绪状态。一种特别简单的生物反馈形式可以通过游戏来测量按钮被按下的压力: 这已被证明与玩家的觉醒水平密切相关; 在天平的另一端是大脑-计算机接口。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。+ 第628行：
第639行： − 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。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。+ 第634行：
第645行： − + 第640行：
第651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。对一大群受试者的结果进行平均，你就可以知道那个商业广告(或电影)是否有预期的效果，以及观众最感兴趣的元素是什么。+ 第652行：
第663行： − + 第658行：
第669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”。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”，并改善以电脑为媒介的人际沟通。它并不一定寻求将情绪映射到一个用于机器解释的客观数学模型中，而是让人类以开放的方式理解彼此的情绪表达，这种方式可能是模糊的、主观的，并且对上下文敏感。+ 第664行：
第675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”。他们说，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670行：
第681行： − 交互式教学法认为，情感虽然具有生物物理性，但它是“有文化基础的、动态体验的，并且在一定程度上是在行动和互动中建构起来的”。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”。+ 第745行：
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全文（除大小标题外）文本第一次翻译
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)和语音描述符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。可能的选择范围很广，有些研究提到使用了200多种不同的特征。识别冗余和不需要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。
情感识别过程的复杂性随着分类器中使用的类(情感)和语音描述符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。可能的选择范围很广，有些研究提到使用了200多种不同的特征【20】。识别冗余和不需要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>
#* Pitch Discontinuity – describes the transitions of the fundamental frequency.
#* Pitch Discontinuity – describes the transitions of the fundamental frequency.
# 频率特性 #
# 频率特性
* 重音形状——受基频变化率的影响。
* 重音形状-受基频变化率的影响。
* 平均音调-描述说话者相对于正常语言的音调高低。#
* 平均音调-描述说话者相对于正常语言的音调高低。
* 等高线斜率-描述频率随时间变化的趋势，可以是上升、下降或水平。#
* 曲线斜率-描述频率随时间变化的趋势，可以是上升、下降或水平。
* 最后降低频率——话语结束时频率下降的幅度。#
* 最后降低频率-话语结束时频率下降的幅度。
* 音高范围-量度一段话语的最高和最低频率之间的差距。# 与时间相关的特征: #
* 音高范围-量度一段话语的最高和最低频率之间的差距。
* 语速-描述在一个时间单位内发出的单词或音节的频率 #
* 2.与时间相关的特征:
* 重音频率-测量出现带有沥青口音的发音的频率 # 语音质量参数和能量描述符: #
* 语速-描述在一个时间单位内发出的单词或音节的频率
* 呼吸质-测量语音中的吸气噪声 #
* 重音频率-测量音高重音出现的频率
* 辉度-描述语音中高频或低频的主导地位 #
* 3.语音质量参数和能量描述符:
* 响度-测量语音的振幅，转换为话音的能量 #
* 呼吸质-测量语音中的吸气噪声
* 暂停间断-描述声音和静音之间的转换 #
* 亮度-描述语音中高频或低频的主导地位
* 响度-测量语音的振幅，转换为话音的能量
* 暂停间断-描述声音和静音之间的转换
* 音高间断-描述基本频率的转换。
* 音高间断-描述基本频率的转换。
The detection and processing of facial expression are achieved through various methods such as optical flow, hidden Markov models, neural network processing or active appearance models. More than one modalities can be combined or fused (multimodal recognition, e.g. facial expressions and speech prosody, facial expressions and hand gestures, or facial expressions with speech and text for multimodal data and metadata analysis) to provide a more robust estimation of the subject's emotional state. Affectiva is a company (co-founded by Rosalind Picard and Rana El Kaliouby) directly related to affective computing and aims at investigating solutions and software for facial affect detection.
The detection and processing of facial expression are achieved through various methods such as optical flow, hidden Markov models, neural network processing or active appearance models. More than one modalities can be combined or fused (multimodal recognition, e.g. facial expressions and speech prosody, facial expressions and hand gestures, or facial expressions with speech and text for multimodal data and metadata analysis) to provide a more robust estimation of the subject's emotional state. Affectiva is a company (co-founded by Rosalind Picard and Rana El Kaliouby) directly related to affective computing and aims at investigating solutions and software for facial affect detection.
面部表情的检测和处理通过光流、隐马尔可夫模型、神经网络处理或主动外观模型等多种方法实现。可以组合或融合多种模态（多模态识别，例如面部表情和语音韵律【27】、面部表情和手势【28】，或用于多模态数据和元数据分析的带有语音和文本的面部表情），以提供对受试者情绪的更可靠估计状态。Affectiva 是一家与情感计算直接相关的公司(由 Rosalind Picard 和 Rana El Kaliouby 共同创办) ，旨在研究面部情感检测的解决方案和软件。
==== Facial expression databases ====
==== Facial expression databases ====
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，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出的面部表情。在姿势表情数据库中，参与者被要求展示不同的基本情绪表情，而在自发表情数据库中，表情是自然的。自发的情绪诱导需要在选择合适的刺激物时付出巨大的努力，这会导致丰富的预期情绪的展示。其次，该过程涉及由受过训练的个人手动标记情绪，这使得数据库高度可靠。 由于对表达及其强度的感知本质上是主观的，专家的注释对于验证的目的是必不可少的。
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.
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.
研究人员使用三种类型的数据库，例如仅峰值表达图像的数据库、描绘从中性到峰值的情绪的图像序列数据库以及带有情绪注释的视频剪辑。面部表情数据库是面部表情识别领域的一个重要研究课题。两个广泛使用的数据库是 CK+和 JAFFE。
====Emotion classification====
====Emotion classification====
He therefore officially put forth six basic emotions, in 1972:
He therefore officially put forth six basic emotions, in 1972:
1960 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】：
* [[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，这些情绪并非都编码在面部肌肉中。新增的情绪是:
<nowiki>#</nowiki> 娱乐
<nowiki>#</nowiki> 轻蔑
<nowiki>#</nowiki> 满足
<nowiki>#</nowiki> 尴尬
<nowiki>#</nowiki> 兴奋
<nowiki>#</nowiki> 内疚
<nowiki>#</nowiki> 成就骄傲
<nowiki>#</nowiki> 解脱
<nowiki>#</nowiki> 满足
<nowiki>#</nowiki> 感官愉悦
<nowiki>#</nowiki> 羞耻
====Facial Action Coding System====
====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”) :
{| class="wikitable sortable"
{| class="wikitable sortable"
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，或者更新的方法如细菌觅食优化算法。聪明的算法。“细菌觅食优化算法-群算法-巧妙算法”。聪明的算法。2011年3月21日。「软电脑」。软计算。2011年3月18日。
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】。
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
其他的挑战
* 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。
* 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
===Body gesture===
===Body gesture===
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.
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。
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.
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】。
===Physiological monitoring===
===Physiological monitoring===
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.
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====
====Blood volume pulse====
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.
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过程来测量，这个过程产生一个图表来显示通过四肢的血液流动。皮卡德，罗莎琳德(1998)。情感计算。麻省理工学院。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。
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。
=====Methodology=====
=====Methodology=====
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。
[[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====
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。
[[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====
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) ，并用于辨别自主觉醒的微小变化。一个主体越兴奋，皮肤导电反应就越强烈。
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】。
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.
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】。
=====Methodology=====
=====Methodology=====
Approaches are based on facial color changes. Delaunay triangulation is used to create the triangular local areas. Some of these triangles which define the interior of the mouth and eyes (sclera and iris) are removed. Use the left triangular areas’ pixels to create feature vectors. It shows that converting the pixel color of the standard RGB color space to a color space such as oRGB color spaceM. Bratkova, S. Boulos, and P. Shirley, oRGB: a practical opponent color space for computer graphics, IEEE Computer Graphics and Applications, 29(1):42–55, 2009. or LMS channels perform better when dealing with faces.Hadas Shahar, Hagit Hel-Or, Micro Expression Classification using Facial Color and Deep Learning Methods, The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0–0. So, map the above vector onto the better color space and decompose into red-green and yellow-blue channels. Then use deep learning methods to find equivalent emotions.
Approaches are based on facial color changes. Delaunay triangulation is used to create the triangular local areas. Some of these triangles which define the interior of the mouth and eyes (sclera and iris) are removed. Use the left triangular areas’ pixels to create feature vectors. It shows that converting the pixel color of the standard RGB color space to a color space such as oRGB color spaceM. Bratkova, S. Boulos, and P. Shirley, oRGB: a practical opponent color space for computer graphics, IEEE Computer Graphics and Applications, 29(1):42–55, 2009. or LMS channels perform better when dealing with faces.Hadas Shahar, Hagit Hel-Or, Micro Expression Classification using Facial Color and Deep Learning Methods, The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 0–0. So, map the above vector onto the better color space and decompose into red-green and yellow-blue channels. Then use deep learning methods to find equivalent emotions.
方法是基于面部颜色的变化。 Delaunay 三角剖分用于创建三角形局部区域。 一些定义嘴巴和眼睛（巩膜和虹膜）内部的三角形被移除。 使用左三角区域的像素来创建特征向量【40】。它表明，将标准 RGB 颜色空间的像素颜色转换为 oRGB 颜色空间【41】或 LMS 通道等颜色空间在处理人脸时表现更好【42】。因此，将上面的矢量映射到较好的颜色空间，并分解为红绿色和黄蓝色通道。然后使用深度学习的方法来找到等效的情绪。
===Visual aesthetics===
===Visual aesthetics===
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。
==Potential applications==
==Potential applications==
http://www.learntechlib.org/p/173785/
http://www.learntechlib.org/p/173785/
情感影响学习者的学习状态。利用情感计算技术，计算机可以通过学习者的面部表情识别来判断学习者的情感和学习状态。在教学中，教师可以利用分析结果了解学生的学习和接受能力，制定合理的教学计划。同时关注学生的内心感受，有利于学生的心理健康。特别是在远程教育中，由于时间和空间的分离，师生之间缺乏双向交流的情感激励。没有了传统课堂学习带来的氛围，学生很容易感到无聊，影响学习效果。将情感计算应用于远程教育系统可以有效地改善这种状况【44】。
=== Healthcare ===
=== Healthcare ===
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/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。
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】。
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>
Affective computing is also being applied to the development of communicative technologies for use by people with autism.Projects in Affective Computing The affective component of a text is also increasingly gaining attention, particularly its role in the so-called emotional or emotive Internet.Shanahan, James; Qu, Yan; Wiebe, Janyce (2006). Computing Attitude and Affect in Text: Theory and Applications. Dordrecht: Springer Science & Business Media. p. 94.
Affective computing is also being applied to the development of communicative technologies for use by people with autism.Projects in Affective Computing The affective component of a text is also increasingly gaining attention, particularly its role in the so-called emotional or emotive Internet.Shanahan, James; Qu, Yan; Wiebe, Janyce (2006). Computing Attitude and Affect in Text: Theory and Applications. Dordrecht: Springer Science & Business Media. p. 94.
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 ===
=== 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】。
=== Other applications ===
=== Other applications ===
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，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】。
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)。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。
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。
==Cognitivist vs. interactional approaches==
==Cognitivist vs. interactional approaches==
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。
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】。
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"/> 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"/> 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】。
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。
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">{{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><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">{{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><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".
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】。
==See also==
==See also==
<small>This page was moved from [[wikipedia:en:Affective computing]]. Its edit history can be viewed at [[情感计算/edithistory]]</small></noinclude>
<small>This page was moved from [[wikipedia:en:Affective computing]]. Its edit history can be viewed at [[情感计算/edithistory]]</small></noinclude>
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