微表情--表情识别研究(英文)
微表情英文讲稿
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04 微表情与心理学
心理学中的微表情研究
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微表情研究的历史背景
微表情研究始于20世纪70年代,旨在探索人类情感和行为的微妙变化。
02
微表情的定义与分类
微表情是一种短暂且无法控制的情绪表达,通常持续时间少于1/2秒。
根据其表现形式,微表情可分为高兴、悲伤、愤怒等类型。
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微表情的测量方法
测量微表情的方法包括视频分析、面部肌肉电活动测量和机器学习等。
微表情与情绪的关系
微表情与基本情绪
微表情与基本情绪之间存在密切 关系,如快乐、悲伤、愤怒等。
微表情与复合情绪
复合情绪如羞耻、尴尬等也可以通 过微表情来表达。
微表情与情绪调节
微表情可以反映一个人的情绪调节 能力,如自我控制和情感表达。
微表情与人格特质的关系
微表情与外向性
外向的人通常更善于表达自己的 情感,因此他们的微表情也更丰
富。
微表情与神经质
神经质的人往往更难以控制自己 的情绪,因此他们的微表情更容
易被观察到。
微表情与开放性
开放性的人更富有想象力和创造 力,他们的微表情也更具有多样
性和复杂性。
05 微表情的实践应用
在人际交往中的应用
读懂他人情绪
通过观察微表情,可以更准确地判断他人的真实 情绪,避免误解和冲突。
提高沟通效果
微表情是一种短暂的、无意识的面部 表情,通常在人们试图掩饰或隐藏真 实情感时出现。
微表情的重要性
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揭示真实情感
微表情能够揭示人们试图 隐藏的真实情感,因为它 们不受意识控制,更能反 映人的内心感受。
增进人际交往
了解微表情有助于更好地 理解他人的感受和情绪, 促进人际关系的建立和维 护。
微表情分析1
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『微表情分析』Surprise1. Eyebrows raised2. Eyes widened3. Mouth Open惊讶:1. 双眉上抬2. 双眼圆睁3. 双唇微启Sadness1. Drooping upper eyelids2. Losing focus in eyes3. Slight pulling down of lip corners 悲伤:1. 上眼睑下垂2. 两眼无神3. 嘴角微微下垂Happiness1. Crow’s f eet wrinkles2. Pushed up cheeks3. Movement from muscle that orbits the eye快乐:1. 产生鱼尾纹2. 两颊抬高3. 眼周肌肉的变化Fear1. Eyebrows raised and pulled together2. Raised upper eyelids3. Tensed lower eyelids4. Lips slightly stretched horizontally back to ears害怕:1. 眉毛抬升并紧蹙2. 上眼睑抬升3. 下眼睑紧绷4. 嘴唇稍许超耳朵水平方向后拉Disgust1. Nose wrinkling2. Upper lip raised厌恶:1. 皱鼻子2. 上唇翻起Contempt1. Lip corner tightened and raised on only one side of face 轻蔑:1. 嘴角抿紧,并仅在脸的一侧上扬Anger1. Eyebrows down and together2. Eyes glare3. Narrowing of the lips愤怒:1. 眉头向下紧蹙2. 怒目瞪视3. 双唇紧抿『识别谎言技巧摘录:』表情·眉毛上挑并挤在一起表示恐惧。
·真正的吃惊表情转瞬即逝,超过一秒钟便是假装的。
lietome微表情动作识别汇总
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1暗自微笑2单肩松动极不自信,在说谎3吃惊的表情持续时间长于1秒,表示他们在撒谎45非常的不屑6羞愧7轻视8为了想起正确答案,他中断了眼神交流。
但是在说一件事时,他没有回避,说明在撒谎9在问他有你以前去过她家吗,他回答不,我以前没有去过她家机械重复在撒谎。
10嘴角向下11眉毛向下是悲伤1112抬起下巴表示很尴尬1213摇头之前先轻轻的点头14一直把左手插到裤袋里顶着大腿,表示很紧张15拒绝的姿势16生理逃跑,很害怕第二集1后退一步抱胸,在说谎2正说,倒说33抿嘴动作,对自己说的没信心,撒谎4轻视5撒谎6伸下巴,生气7眉毛上扬而且紧凑,说明很害怕88第三集1说悲伤的时候额头上没有相应的反应2两边不对称,极有可能表情是装出来的3摩擦自己的手,自我安慰,不相信自己说什么,使自己安心。
第四集眉毛向下皱在一起,眼脸上扬,眼袋紧绷,非常愤怒第五集:1谎言没准备会时间很长,但是如果已经准备好,会迫不及待讲出来2鼻孔外翻,嘴唇紧闭,生气没被尊重3内疚4嘴角下垂下巴扬起,自责5她在逼自己不把事情说出来,很矛盾自己说不说6说自己相信的时候微微摇头,在撒谎7眉毛向上拉紧了,恐惧78弓起身子,撒谎的表现9手不经意握了一下,表示在撒谎10试图使自己不供出来11咽了一口水,强烈情感的表现12摸了一下耳朵,撒谎第七集1强势的表示显示自己没说实话。
2咬嘴唇,焦躁3她在说研究时,他看着她,然而她望向他时,他低头了,内疚第八集1目光向下然后转开,羞愧2每次都在转换动词3负面的词语,内疚4眉毛上扬表示,知道正确答案5转移话题,在撒谎第九集1眼睛在眨动,隐瞒了一些事2碰触,控制欲第十集1轻蔑2摸鼻子。
撒谎。
3对自己说的话没信心,说话会比较轻4撅嘴,被说法伤害了第十一集1挺下巴挺胸,自信。
2鼻孔抽动,快速吸气。
3奇怪的愤怒,奇怪的愤怒来得快去得快第二季第一集1撅嘴直视,在撒谎2屏住呼吸,焦躁第二集1语境嵌入,说的是实话第四集11把球拿在胸前,形成屏障,焦躁的表现2愧疚3称呼换了,说不的时候太快了,并且音调有提高4嘴巴紧闭,眼睛睁大,撒谎。
微表情测量工具——FACS(老王整理编写,最完整)
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微表情测量工具——FACS(FACIAL ACTION CODING SYSTEM)(老王整理撰写)1+2+5+25+26=惊讶4+5+24=愤怒这样的数学题估计没多少人可以求证出来。
但是对于情绪心理学的研究者来说,这是心理学上的一个创举,也是对人类的一大贡献,它的名字叫FACS(FACIAL ACTION CODING SYSTEM),中文翻译叫做“面部运动编码系统”。
Paul Ekman,这个名字有人很陌生有人确很熟悉,如果看过美剧《Lie to me》一定为主角Cal Lightman那神乎其技的“读心”能力印象深刻。
其实,该剧的幕后心理学顾问就是Ekman教授,并且Lightman的背景是根据他来改编而成的。
对微表情非常感兴趣的朋友们应该对他有所了解,也肯定读过他的书——《说谎》,《情绪的解析》等等。
就是这样一个伟大的心理学家在1976年和研究伙伴作了深入的研究,(通过观察和生物反馈)他们描绘出了不同的脸部肌肉动作和不同表情的对应关系从而创制了FACS。
他们根据人脸的解剖学特点,将其划分成若干既相互独立又相互联系的运动单元(AU——Action Unit),并分析了这些运动单元的运动特征及其所控制的主要区域以及与之相关的表情,并给出了大量的照片说明。
FACS 该套系统将许多现实生活中人类的表情进行了分类,它是如今面部表情的肌肉运动的权威参照标准,也被心理学家和动画片绘画者使用。
有人会问:“我们又不是心理学家,也不是FBI或者警察,学这个对平时生活有什么用”其实道理很简单,对于FACS可学可不学,但是对于向往更精确的观察表情,对之后表情分析有重大意义,FACS会帮助我们对面部的运动了解的更深入,不容易出现张冠李戴的问题。
举个例子来说:对比这两张图,左侧是6+12,右侧是6+12+17。
多了一块肌肉的运动,情绪就会差很多,虽然不学FACS也能看出区别,但如果右侧的17的强度很小呢是不是会忽略一些情绪的信息我想可能会的。
微表情 ppt课件
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2、医学领域 精神分裂症患者可以从METT训练中受益,使得他们的微表情识别恢复到正常。
3、心理分析 微表情,是心理应激微反应的一部分。
PPT课件
因此,以微表情为代表的微反应是个人内心想法的忠实呈现,是了解一个人内 心真实想法的最准确线索。在日常生活中,这种能力也能帮助我们。
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lie to me(别对我 《微表情与身体语言
主要故事情节来自美国心理学专家保罗?艾克曼博士其主要研究方向为人类面部表情的辨识情绪分析与人际欺骗人的脸部可以传输信息它是媒介是信息传输器
PPT课件
微表情
——Lie to me
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《别对我说谎》是 一部描述心理学的 美国电视剧,于 2009年1月21日 首播于福克斯电视 网。主要故事情节 来自美国心理学专 家保罗•艾克曼博 士,其主要研究方 向为人类面部表情 的辨识、情绪分析 与人际欺骗等。
会倒着想。
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1、微表情训练 2002 年, 微表情识别领域取得了重大进展, Ekman 研制出第一个微 表情训练工具(Micro Expression Training Tool, METT)。但是, METT 的训练效果的维持时间长短目前还不得而知。
2、瞬间互动研究 在二十世纪六十年代,美国心理学家William Condon率先进行了 针对瞬间互动的研究。通过对情侣录像来分析两人间的互动。通过 研究这些微动作,可以预言哪些情侣会继续恋情,而哪些将会分PP手T课。件
PPT课件
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● 微表情(micro—expression)
● 是内心的流露与掩饰,心理学名词。
● 人的脸部可以传输信息,它是媒介,是信息传输器。比起人们有
意识做出的表情,"微表情"更能体现人们真实的感受和动机。 虽然
微表情研究论文
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微表情研究论文由于微表情非常微小且瞬间发生,因此对其进行研究是非常困难的。
在最近的几年中,人们对微表情的研究逐渐增加。
以下是一些与微表情相关的研究论文:1. “Micro-Momentary Facial Expressions as Indicators of Motivation to Quit Smoking”(Pekrun R, Oliver M, Hekeren J,从微观上研究微表情是否能够作为戒烟动机的指标)这项研究利用电子脸部分析仪对吸烟者进行了实时监测,以揭示微动作是否能够作为烟民主观戒烟积极性的指标。
结果表明,微表情可以作为有效的戒烟动机指标。
2. “Micro-Expressions in 60 Minutes: Mixed Method Analysis of Movie 'Whistleblower'”(从电影《揭密者》中探究微表情的分析方法)该研究使用混合研究方法,结合定量和定性分析,研究如何从电影中准确捕捉和分析微微的脸部表情。
结果显示,微表情可以通过电影中的画面,来进行分类和描述。
3. “Micro-Expressions and the Halo Effect in Interpersonal Perception”(在人际关系中进行微表情研究)在这项研究中,对人际关系中的微表情进行了深入探讨,以研究微表情是否会影响人们对个体的整体印象。
结果表明,微表情能够影响人们的整体评价。
4. “Non-verbal behavior in psychotherapy: the influence of micro-expressions on the perception of th erapist competence”(探究微表情对治疗师能力评价的影响)这项研究分析了在心理治疗中,微表情的作用。
结果表明,治疗师的微表情可以影响其能力的评价。
总的来说,微表情的研究还处于发展的初期,但是这些研究已经为我们提供了有价值的洞见,有助于人们更好地理解和研究微观表情。
微表情识别技术研究
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微表情识别技术研究随着社交媒体和在线视频的普及,人们交流和表达情感的方式也在不断变化。
在人际关系中,非语言沟通的作用至关重要。
而微表情是一种极为微小的面部表情,短暂地显现出人的情绪情感。
微表情既能够在一定程度上反映出个体内在的情感状态,又能够与言语、肢体动作等多种线索相互印证,从而加深对个体情感状态的判断。
由此,微表情识别技术已经越来越受到各个领域的关注。
一、微表情的定义及背景微表情(microexpression)是一种极为微小、短暂而快速的面部表达方式,平均持续时间约为0.2秒,关键特征在于其意义明显,具有突然性、反常性和短暂性。
微表情是情感表达的关键,但是由于其短暂性和微小性,很难被普通人识别。
眨眼、抿嘴等微小表情的识别要求识别者对情感表达的敏锐度和专门训练。
因此,微表情识别技术应运而生。
它是对微表情的人工智能分析,为研究者提供了更多客观的数据,从而帮助研究者更好地了解和分析人类情感交流的本质和变化。
二、微表情识别技术的相关研究微表情识别技术的研究主要包括两个方面:认知特点研究和计算机视觉技术研究。
1.认知特点研究早期研究表明,微表情共有七种类型:愤怒、厌恶、畏惧、惊讶、快乐、悲伤和压抑。
换言之,微表情可以直接反映出人的情感状态和心理变化,进一步加深对个体心理状态的判断,因此可应用于情绪识别、视觉疲劳检测、人际交往分析等领域。
2.计算机视觉技术研究同时,计算机视觉技术的发展,使得微表情的识别可以被计算机更快、更准确地实现。
目前,在微表情识别技术的研究方面,主要的方法有:面部特征提取、动态模型预测、特征描述分析、分类器构建等。
其中,面部特征提取是最基本也是最核心的一项内容,也是计算机端最早使用的一种方法。
面部特征提取包括目标检测、特征点定位等处理操作,准确度与处理时间的长短因目标场景和处理器性能而异。
三、微表情识别技术在实际应用中的情况作为一种新兴的技术,微表情识别技术具备着广泛的应用前景。
学习、工作、生活中的微表情识别现象(面部表情、身段表情、时空
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学习、工作、生活中的微表情识别现象(面部表情、身段表情、时空1微表情与人的沟通人的沟通方式有两种,一种是言语的沟通,比如面对面的谈话,发信息等;另一种是非言语的沟通,主要通过人的表情、姿态、语气语调等进行。
有人做过统计,在人与人的沟通交流中,表情的作用占了55%。
可见表情对于我们表达自身情感信息的对非言语性行为是非常重要的,可视为人类心理活动的晴雨表。
关于人类表情的研究可以追溯到进化论之父达尔文,除了《物种起源》之外,他还写过一本书《人与动物的表情》。
时至今日,我们对表情的研究已经非常丰富,比如确定了人类的六大基本表情,高兴、厌恶、愤怒、恐惧、悲伤、惊讶。
之所以称之为人类基本表情,是因为这几种表情具有跨种族的一致性,甚至在我们的近亲黑猩猩身上也能看到。
而近些年来,关于表情最惊喜的发现莫过于心理学家们发现微表情的存在。
一个偶然的机会,美国心理学家艾克曼(Ekman)和弗里森(Friesen)(1969)受一位精神病学家的委托,对一段抑郁症患者撒谎以掩盖其自杀意图的录像进行检测。
然而,艾克曼和弗里森起初并未从这段视频中发现该患者有任何异常表现:该患者显得很乐观,笑得很多,表面上没有表现出任何企图自杀的迹象。
但当对该录像进行慢速播放并逐帧进行检查时,他们发现在回答一声提出的关于未来计划的问题时,该患者出现了一个强烈的痛苦的表情。
这个表情持续时间仅为1/12秒,二人称之为微表情。
微表情是人类试图压抑或隐藏真实情感时泄露的非常短暂的、不能自主控制的面部表情。
它与普通表情的区别在于,微表情持续时间很短,仅为1/25秒至1/5秒。
因此,大多数人往往难以觉察到它的存在。
这种快速出现不易被察觉的面部表情被认为与自我防御机制有关,表达了被压抑的情绪。
微表情既可能包含普通表情的全部肌肉动作,也可能只包含普通表情肌肉动作的一部分,它是一种自发性的表情动作,表达了六大基本表情。
由于微表情能够表达被压抑掩藏的真实的情绪,因此往往被视为很好的谎言识别的有效线索。
微表情识别研究综述
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面部表情作为人表现情感的主要方式之一,在过去的几十年里,关于各种表情识别的研究已经取得重要的进展[1-6]。
这几年,关于自发式的表情(spontaneous expression)的识别成为了新的研究热点[7-8],而微表情往往是在人想压抑自己感情时产生的,这既无法伪造也无法抑制[9]。
完整的面部表情通常持续0.5~4s[10],比较容易被人识别。
然而,心理学认为,当一个人试图隐藏自己真实情感时,偶尔会有情感泄露出来。
微表情首次发现于1966年[11]。
三年后,Ekman等人[12]在分析一段试图自杀的病人的采访视频时使用了微表情这个词。
微表情通常在1/25~1/2s[13]之间不受控制地变化,并且出现频率较低,未经过训练的个体对其识别能力并不高[14]。
而不同的研究者报告的结果也存在着较大的差异[15-16]。
在这之后,Ekman和Friesen于1979年提出了短暂表情识别测验(Brief Affect Recognition Test,BART)[17]。
在后续的实验中他们发现了被试者的微表情识别能力与谎言识别能力成正相关[18]。
之后,进行了日本人与高加索人短暂表情识别测验(Japanese and Caucasian Brief Affect Recognition Test,JACBART)[19-20],该实验也验证了被试者的微表情识别能力与谎言识别能力成正相微表情识别研究综述张人1,何宁21.北京联合大学北京市信息服务工程重点实验室,北京1001012.北京联合大学智慧城市学院,北京100101摘要:微表情是人类在试图掩饰自己情感时所产生的面部细微变化,在测谎、安防、心理学治疗和微表情识别机器人等方面有着非常广泛的应用,因此微表情识别也开始得到重视。
从微表情识别的主流的方法:卷积神经网络及其改进、光流法及其改进、局部二值模式及其改进方法进行分析,对现存的几种方法从使用的算法、准确率、各方法的优缺点、各方法的特点等几个角度进行对比总结;阐述微表情识别目前存在的问题,并对未来的发展方向进行展望。
微表情识别:跨域适应的挑战与创新
![微表情识别:跨域适应的挑战与创新](https://img.taocdn.com/s3/m/9948010f76232f60ddccda38376baf1ffc4fe3d1.png)
微表情识别:跨域适应的挑战与创新标题:微表情识别:跨域适应的挑战与创新在人类情感的微妙世界里,微表情(Micro-expressions)如同隐藏在微笑背后的一抹泪光,短暂而难以捉摸。
它们是人在试图隐藏真实情感时不自觉露出的面部表情,持续时间极短,通常不超过半秒,却蕴含着巨大的情感信息。
近年来,随着人工智能技术的飞速发展,微表情识别(Micro-Expression Recognition, MER)逐渐成为研究的热点,其在测谎、情感分析等领域的应用前景令人期待。
然而,微表情识别并非易事。
微表情的捕捉和识别面临着许多挑战,其中之一便是跨域适应问题。
所谓跨域适应,指的是训练数据(源域)和测试数据(目标域)来自不同的分布,这在现实世界中极为常见。
例如,训练数据可能来自一种摄像设备,而测试数据则来自另一种设备,或者训练和测试数据是在不同的光照、背景条件下收集的。
这种域间的差异会导致传统识别方法的性能大打折扣。
为了解决这一难题,研究者们提出了无监督域适应(Unsupervised Domain Adaptation, UDA)方法。
这些方法的核心思想是找到一个映射,使得源域和目标域的数据在新的特征空间中具有相似的分布。
最近,一种名为联合块加权和矩匹配(Joint Patch Weighting and Moment Matching, JPMM)的新方法在这一领域脱颖而出。
JPMM方法通过最小化源域和目标域微表情特征集的概率分布差异,实现了域间的桥接。
它不仅考虑了有助于区分不同微表情的面部块的贡献,还通过多阶矩匹配操作来减少特征分布的不一致性。
这种方法的提出,不仅在理论上具有创新性,而且在实践中也显示出了卓越的性能。
深入探索在深入探索JPMM方法之前,让我们先回顾一下微表情识别的发展历程。
早期的方法主要依赖于手工设计的特征,如局部二值模式(Local Binary Patterns, LBP)和光流(Optical Flow)等。
人脸表情识别英文参考资料
![人脸表情识别英文参考资料](https://img.taocdn.com/s3/m/230aded881c758f5f71f6717.png)
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IEEE Transactions on Multimedia, 2010, 12(6): 544 - 551[J32]Kotsia I, Pitas I, Zafeiriou S, Zafeiriou S. Novel Multiclass Classifiers Based on the Minimization of the Within-Class Variance. IEEE Transactions on Neural Networks, 2009, 20(1): 14 - 34[J33]Cohen I, Cozman F.G, Sebe N, Cirelo M.C, Huang T.S. Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(12): 1553 - 1566[J34] Zafeiriou S. Discriminant Nonnegative Tensor Factorization Algorithms. IEEE Transactions on Neural Networks, 2009, 20(2): 217 - 235 [J35] Zafeiriou S, Petrou M. Nonlinear Non-Negative Component Analysis Algorithms. IEEE Transactions on Image Processing, 2010, 19(4): 1050 - 1066[J36] Kotsia I, Zafeiriou S, Pitas I. A Novel Discriminant Non-Negative Matrix Factorization Algorithm With Applications to Facial Image Characterization Problems. IEEE Transactions on Information Forensics and Security, 2007, 2(3): 588 - 595[J37] Irene Kotsia, Stefanos Zafeiriou, Ioannis Pitas. Texture and shape information fusion for facial expression and facial action unit recognition. Pattern Recognition, 2008, 41(3): 833-851[J38]Wenfei Gu, Cheng Xiang, Y.V. Venkatesh, Dong Huang, Hai Lin. Facial expression recognition using radial encoding of local Gabor features andclassifier synthesis.Pattern Recognition, In Press, Corrected Proof, Available online 27 May 2011[J39] F Dornaika, E Lazkano, B Sierra. Improving dynamic facial expression recognition with feature subset selection.Pattern Recognition Letters, 2011, 32(5): 740-748[J40] Te-Hsun Wang, Jenn-Jier James Lien. Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation. Pattern Recognition, 2009, 42(5): 962-977[J41] Hyung-Soo Lee, Daijin Kim.Expression-invariant face recognition by facial expression transformations.Pattern Recognition Letters, 2008, 29(13): 1797-1805[J42] Guoying Zhao, Matti Pietikäinen. Boosted multi-resolution spatiotemporal descriptors for facial expression recognition. Pattern Recognition Letters, 2009, 30(12): 1117-1127[J43] Xudong Xie, Kin-Man Lam. Facial expression recognition based on shape and texture. Pattern Recognition, 2009, 42(5):1003-1011[J44] Peng Yang, Qingshan Liu, Dimitris N. Metaxas Boosting encoded dynamic features for facial expression recognition. Pattern Recognition Letters, 2009,30(2): 132-139[J45] Sungsoo Park, Daijin Kim. Subtle facial expression recognition using motion magnification. Pattern Recognition Letters, 2009, 30(7): 708-716[J46] Chathura R. De Silva, Surendra Ranganath, Liyanage C. De Silva. Cloud basis function neural network: A modified RBF network architecture for holistic facial expression recognition.Pattern Recognition, 2008, 41(4): 1241-1253[J47] Do Hyoung Kim, Sung Uk Jung, Myung Jin Chung. Extension of cascaded simple feature based face detection to facial expression recognition.Pattern Recognition Letters, 2008, 29(11): 1621-1631[J48] Y. Zhu, L.C. De Silva, C.C. Ko. Using moment invariants and HMM in facial expression recognition. Pattern Recognition Letters, 2002, 23(1-3): 83-91[J49] Jun Wang, Lijun Yin. Static topographic modeling for facial expression recognition and puter Vision and Image Understanding, 2007, 108(1-2): 19-34[J50] Caifeng Shan, Shaogang Gong, Peter W. McOwan. Facial expression recognition based on Local Binary Patterns: A comprehensive study. Image and Vision Computing, 2009, 27(6): 803-816[J51] Xue-wen Chen, Thomas Huang. Facial expression recognition: A clustering-based approach. Pattern Recognition Letters, 2003, 24(9-10): 1295-1302[J52] Irene Kotsia, Ioan Buciu, Ioannis Pitas.An analysis of facial expression recognition under partial facial image occlusion. Image and Vision Computing, 2008, 26(7): 1052-1067[J53] Shuai Liu, Qiuqi Ruan. Orthogonal Tensor Neighborhood Preserving Embedding for facial expression recognition.Pattern Recognition, 2011, 44(7):1497-1513[J54] Eszter Székely, Henning Tiemeier, Lidia R. Arends, Vincent W.V. Jaddoe, Albert Hofman, Frank C. Verhulst, Catherine M. Herba. Recognition of Facial Expressions of Emotions by 3-Year-Olds. Emotion, 2011, 11(2): 425-435[J55] Kathleen M. Corcoran, Sheila R. Woody, David F. Tolin.Recognition of facial expressions in obsessive–compulsive disorder. Journal of Anxiety Disorders, 2008, 22(1): 56-66[J56] Bouchra Abboud, Franck Davoine, MôDang. Facial expression recognition and synthesis based on an appearance model. Signal Processing: Image Communication, 2004, 19(8): 723-740[J57] Teng Sha, Mingli Song, Jiajun Bu, Chun Chen, Dacheng Tao. Feature level analysis for 3D facial expression recognition. Neurocomputing, 2011, 74(12-13) :2135-2141[J58] S. Moore, R. Bowden. Local binary patterns for multi-view facial expression recognition. Computer Vision and Image Understanding, 2011, 15(4):541-558[J59] Rui Xiao, Qijun Zhao, David Zhang, Pengfei Shi. Facial expression recognition on multiple manifolds.Pattern Recognition, 2011, 44(1):107-116[J60] Shyi-Chyi Cheng, Ming-Yao Chen, Hong-Yi Chang, Tzu-Chuan Chou. Semantic-based facial expression recognition using analytical hierarchy process.Expert Systems with Applications, 2007, 33(1): 86-95[J71] Carlos E. Thomaz, Duncan F. Gillies, Raul Q. Feitosa. Using mixture covariance matrices to improve face and facial expression recognitions. Pattern Recognition Letters, 2003, 24(13): 2159-2165[J72]Wen G,Bo C,Shan Shi-guang,et al. The CAS-PEAL large-scale Chinese face database and baseline evaluations.IEEE Transactions on Systems,Man and Cybernetics,part A:Systems and Hu-mans,2008,38(1):149-161. [J73] Yongsheng Gao,Leung ,M.K.H. Face recognition using line edge map.IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24:764-779.[J74] Hanouz M,Kittler J,Kamarainen J K,et al. Feature-based affine-invariant localization of faces.IEEE Transactions on Pat-tern Analysis and Machine Intelligence,2005,27:1490-1495.[J75] WISKOTT L,FELLOUS J M,KRUGER N,et al.Face recognition by elastic bunch graph matching.IEEE Trans on Pattern Analysis and Machine Intelligence,1997,19(7):775-779.[J76] Belhumeur P.N, Hespanha J.P, Kriegman D.J. Eigenfaces vs. fischerfaces: recognition using class specific linear projection.IEEE Trans on Pattern Analysis and Machine Intelligence,1997,15(7):711-720 [J77] MA L,KHORASANI K.Facial Expression Recognition Using ConstructiveFeedforward Neural Networks. 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lie to me微表情动作识别汇总(图+文字)(别对我撒谎)之欧阳歌谷创作
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1暗自微笑欧阳歌谷(2021.02.01)2单肩松动极不自信,在说谎3吃惊的表情持续时间长于1秒,表示他们在撒谎45非常的不屑6羞愧7轻视8为了想起正确答案,他中断了眼神交流。
但是在说一件事时,他没有回避,说明在撒谎9在问他有你以前去过她家吗,他回答不,我以前没有去过她家机械重复在撒谎。
10嘴角向下11眉毛向下是悲伤1112抬起下巴表示很尴尬1213摇头之前先轻轻的点头14一直把左手插到裤袋里顶着大腿,表示很紧张15拒绝的姿势16生理逃跑,很害怕第二集1后退一步抱胸,在说谎2正说,倒说33抿嘴动作,对自己说的没信心,撒谎4轻视5撒谎6伸下巴,生气7眉毛上扬而且紧凑,说明很害怕88第三集1说悲伤的时候额头上没有相应的反应2两边不对称,极有可能表情是装出来的3摩擦自己的手,自我安慰,不相信自己说什么,使自己安心。
第四集眉毛向下皱在一起,眼脸上扬,眼袋紧绷,非常愤怒第五集:1谎言没准备会时间很长,但是如果已经准备好,会迫不及待讲出来2鼻孔外翻,嘴唇紧闭,生气没被尊重3内疚4嘴角下垂下巴扬起,自责5她在逼自己不把事情说出来,很矛盾自己说不说6说自己相信的时候微微摇头,在撒谎7眉毛向上拉紧了,恐惧78弓起身子,撒谎的表现9手不经意握了一下,表示在撒谎10试图使自己不供出来11咽了一口水,强烈情感的表现12摸了一下耳朵,撒谎第七集1强势的表示显示自己没说实话。
2咬嘴唇,焦躁3她在说研究时,他看着她,然而她望向他时,他低头了,内疚第八集1目光向下然后转开,羞愧2每次都在转换动词3负面的词语,内疚4眉毛上扬表示,知道正确答案5转移话题,在撒谎第九集1眼睛在眨动,隐瞒了一些事2碰触,控制欲第十集1轻蔑2摸鼻子。
撒谎。
3对自己说的话没信心,说话会比较轻4撅嘴,被说法伤害了第十一集1挺下巴挺胸,自信。
2鼻孔抽动,快速吸气。
3奇怪的愤怒,奇怪的愤怒来得快去得快第二季第一集1撅嘴直视,在撒谎2屏住呼吸,焦躁第二集1语境嵌入,说的是实话第四集11把球拿在胸前,形成屏障,焦躁的表现2愧疚3称呼换了,说不的时候太快了,并且音调有提高4嘴巴紧闭,眼睛睁大,撒谎。
微表情研究
![微表情研究](https://img.taocdn.com/s3/m/bee2d39a4afe04a1b171de21.png)
原因分析
• 其次, Porter 和ten Brinke(2008)使用的诱发微表情 的方法与Ekman使用的方法有所不同。
• 前者采用的却是无恶意谎言(white-lies)范式,被试 撒谎或者不撒谎对其自身均无影响。而后者使用的是 高风险谎言(high-stake Lies)范式(Ekman,2009),包 括三种变式:(1)情绪性谎言(emotional lies)范式;(2) 模拟犯罪(mock crime)范式;(3)信念性谎言(opinion lies)范式。
微表情识别能力
• 为确认微表情识别能力与谎言识别准确性之间 的关系, Frank和Ekman(1997)研制了一个新测 验来进一步考察人们识别微表情的能力。
• 该测验的测试程序与BART完全相同,但使用了一 套新的表情图片,而这套表情图片具有较高的跨 文化一致性。
• 结果发现:微表情识别能力与谎言识别的准确性
• Swart和 Aleman (2009)用METT比较了高述情障 碍特质者和低述情障碍特质者在微表情识别能 力上的差别,结果发现,高述情障碍特质者的微 表情识别能力要低于低述情障碍特质者。
• 因此,不同人群的微表情识别能力的确存在着差 别。在临床上,医生若能识别病人的微表情,则 可以更好地了解病人的需求,针对性地确定治疗 方案,缩短疗程,提高疗效。
• 他们发现,精神分裂症患者与正常人都能从METT训练程 序中获益,情绪识别和微表情识别的能力较训练前均有 显著提高;精神分裂症患者的情绪识别和微表情识别能 力可以恢复到正常人未受训练前的水平。
• 这一结果提示,对精神分裂症患者进行针对性的微表情 识别训练,可有效地缓解其社会功能的损害。
医学临床领域
• 后测程序也与JACBART相同,但使用了与前测不 同的数据集,以测量被试接受训练后的微表情识 别能力。
关于心理学的英语微表情PPT课件
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universal emotions
Analysis of some movements
universal emotions
Disgust
Anger
fear
surprise
contempt
happiness sadness
sadness
drooping upper eyelids Losing focus in eyes Slight pulling down of lip corner
Eyes looked around, afraid to look directly at each other, said tension and fear, have no confidence in their own 眼睛左顾右盼, 不敢直视对方, 表示紧张害怕, 对自己没有信心
捂嘴、舔嘴——撒谎
微表情是人在试图隐藏 或者控制某种情绪时不 自觉地表现出来、且持 续时间很短的一种面部 表情。微表情通常会在 人们经历得失、情势危 急的时候出现。
*Purpose
Maximize the understanding of each other; Master their circumstances; Avoid danger, deception, embarrassment or loss of social status; Induction, confirmation or control each other's ideas;
contempt
disgust
Nose wrinkling
Upper lip raised
THANK
YOU
微表情识别综述
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微表情识别综述作者:***来源:《计算机时代》2020年第09期摘要:微表情的微妙和微表情数据集的通病,对人脸微表情识别任务提出了巨大挑战,同时也使得该课题具有旺盛的生命力和极高的研究价值。
文章阐述了人脸微表情识别的定义,介绍了主流的微表情数据集,并总结了微表情识别领域中基于三个正交平面局部二值模型的经典方法和基于深度学习的最新技术。
关键词:微表情识别;微表情数据集;深度学习;面部动作编码系统;三个正交平面局部二值模型中图分类号:TP391.4文献标识码:A文章编号:1006-8228(2020)09-17-03A survey of micro-expression recognitionCheng Cun(school of MathematicsandStatistics, Beijing Technology and Business Unirersity, Beijing 100048. China)Abstract: Facial micro-expression recognition is faced with an enormous challenge because facial micro-expression is subtle andmicro-expression databases are limited. but in the meantime the significance of micro-expression recognition has posed a hugeattraction to researchers. In this paper,the definition of facial micro-expression recognition is introduced, the commonly used micro-expression databases are summarized, and the classic handcrafted method based on Local Binary Pattern From Three OrthogonalPlanes and the recent techniques based on deep learning in micro-expression recognition are elaborated.Key words: micro-expression recognition; micro-expression database; deep learning; facial action coding system; Local BinaryPattern From Three Orthogonal Planes0引言人臉表情识别(Facial Expression Recognition)是计算机视觉的一个重要研究课题。
lie-to-me微表情动作识别汇总(图 文字)(别对我撒谎)
![lie-to-me微表情动作识别汇总(图 文字)(别对我撒谎)](https://img.taocdn.com/s3/m/b8fb41e6bb4cf7ec4afed0ee.png)
1暗自微笑2单肩松动极不自信,在说谎3吃惊的表情持续时间长于1秒,表示他们在撒谎45非常的不屑6羞愧7轻视8为了想起正确答案,他中断了眼神交流。
但是在说一件事时,他没有回避,说明在撒谎9在问他有你以前去过她家吗,他回答不,我以前没有去过她家机械重复在撒谎。
10嘴角向下11眉毛向下是悲伤1112抬起下巴表示很尴尬1213摇头之前先轻轻的点头14一直把左手插到裤袋里顶着大腿,表示很紧张15拒绝的姿势16生理逃跑,很害怕第二集1后退一步抱胸,在说谎2正说,倒说33抿嘴动作,对自己说的没信心,撒谎4轻视5撒谎6伸下巴,生气7眉毛上扬而且紧凑,说明很害怕88第三集1说悲伤的时候额头上没有相应的反应2两边不对称,极有可能表情是装出来的3摩擦自己的手,自我安慰,不相信自己说什么,使自己安心。
第四集眉毛向下皱在一起,眼脸上扬,眼袋紧绷,非常愤怒第五集:1谎言没准备会时间很长,但是如果已经准备好,会迫不及待讲出来2鼻孔外翻,嘴唇紧闭,生气没被尊重3内疚4嘴角下垂下巴扬起,自责5她在逼自己不把事情说出来,很矛盾自己说不说6说自己相信的时候微微摇头,在撒谎7眉毛向上拉紧了,恐惧78弓起身子,撒谎的表现9手不经意握了一下,表示在撒谎10试图使自己不供出来11咽了一口水,强烈情感的表现12摸了一下耳朵,撒谎第七集1强势的表示显示自己没说实话。
2咬嘴唇,焦躁3她在说研究时,他看着她,然而她望向他时,他低头了,内疚第八集1目光向下然后转开,羞愧2每次都在转换动词3负面的词语,内疚4眉毛上扬表示,知道正确答案5转移话题,在撒谎第九集1眼睛在眨动,隐瞒了一些事2碰触,控制欲第十集1轻蔑2摸鼻子。
撒谎。
3对自己说的话没信心,说话会比较轻4撅嘴,被说法伤害了第十一集1挺下巴挺胸,自信。
2鼻孔抽动,快速吸气。
3奇怪的愤怒,奇怪的愤怒来得快去得快第二季第一集1撅嘴直视,在撒谎2屏住呼吸,焦躁第二集1语境嵌入,说的是实话第四集11把球拿在胸前,形成屏障,焦躁的表现2愧疚3称呼换了,说不的时候太快了,并且音调有提高4嘴巴紧闭,眼睛睁大,撒谎。
关于microexpression(微表情)的PPT
![关于microexpression(微表情)的PPT](https://img.taocdn.com/s3/m/fd9d4c4e2b160b4e767fcfc3.png)
Sadness. This expression features narrowed eyes, eyebrows brought together, a down-turned mouth, and a pulling up or bunching of the chin.
Disgust. A look of disgust includes nose scrunching, raising of the upper lip, downcast eyebrows and narrowed eyes.
Surprise. Surprise appears with a dropped jaw, relaxed lips and mouth, widened eyes and slightly raised eyelids and eyebrows.
Fear. In fear, the mouth and eyes are open, eyebrows are raised and nostrils are sometimes flared.
Contempt. Contempt is notable for its raising of one side of the mouth into a sneer or smirk.
Anger. Anger involves lowered eyebrows, a wrinkled forehead, tensed eyelids and tensed lips.
microexpression(微表情)
• A microexpression is a brief, involuntary facial expression shown on the face of humans when one is trying to conceal or repress an emotion. • Unlike regular facial expressions, few can fake a microexpression. They consist of and completely resemble the seven universal emotions: disgust, anger, fear, sadness, happiness, surprise, and contempt. Microexpressions can occur as fast as 1/25 of a second.
微表情--表情识别研究(英文)
![微表情--表情识别研究(英文)](https://img.taocdn.com/s3/m/e01ea5890975f46527d3e1f4.png)
• 在谎言突然说出时,很多人以为撒谎要花很多时间来反应, 但如果谎言已提前准备好,就会迫不及待说出来
• In a lie suddenly said, many people think that lie to spend a lot of time to respond, but if lies in advance has already, can't wait out.
Hale Waihona Puke 然,有时候善意的谎言还是很好的。柴子 佳
SUCCESS
THANK YOU
2019/10/26
微表情 micro-expression
What's Mirco-Expression ?
•微表情是人 在试图隐藏 或者控制某 种情绪时不 自觉地表现 出来、且持 续时间很短 的一种面部 表情。
• A microexpression is a brief, involuntary facial expression shown on the face of humans when one is trying to
• 回答与提问之间的时间差被称为反应潜伏期,反应潜伏期越 长,说明回答者对真相有所隐瞒
• Answer questions and the time between called reaction incubation period, reaction incubation period is longer, the respondents to hide nothing from that truth
Have you seen American-TV 《lie to me》?
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What's Mirco-Expression ?
•微表情是人 在试图隐藏 或者控制某 种情绪时不 自觉地表现 出来、且持 续时间很短 的一种面部 表情。
• A microexpression is a brief, involuntary facial expression shown on the face of humans when one is trying to
THE MICRO-EXPRESSION IS PSYCHOLOGY NOUNS
• 持续时间在1/25~1/5秒以内的表情是微表情
Ekman(1969)
• 持续时间1/2秒以内的表情都可以被称之为微
日本研究(2000)
• 微表情是一种持续时间仅为1/25秒至1/5秒的 非常快速的表情,表达了人试图压抑与隐撒谎要花很多时间来反应,
但如果谎言已提前准备好,就会迫不及待说出来
• In a lie suddenly said, many people think that lie to spend a lot of time to respond, but if lies in advance has already, can't wait out. • 回答与提问之间的时间差被称为反应潜伏期,反应潜伏期越
吴奇 傅小兰
• In our life,"Micro-Expression" everywhere is visible,
but we often ignore it. Actually human's life with" Micro-Expression“ is concerned, we can look through the implied by the psychological problems of better understanding others, such as personality
Have you seen American-TV 《lie to me》?
micro-expressionsconsist micro-expression of and completely resemble(完全反应) the seven universal emotions: disgust厌恶, anger愤怒, fear, sadness, happiness, surprise, and contempt (蔑视).
微表情识别训练的有效性因人而异
• 研究显示:不同的人群 在微表情识别的能力上 存在着差别。
微表情小测试
1.准备笔和纸
2.写下序号(1. 2. 3. 4. 5. 6. 7. 8.) 3.在纸的最上面写上以下情绪:自然,愤 怒,恐惧,悲伤,厌恶,轻蔑,羞愧,开 心 4.观看以下图片,以最快速度写下你认为 图片所表现的情绪
preferences abhor etc. It can also increase the
people's integrity awareness, sincere to others, to understand other people, to promote the relationship between each other more harmonious, be helpful for social harmony.
当然,有时候善意的谎言还是很好的。
柴子佳
12063236
长,说明回答者对真相有所隐瞒
• Answer questions and the time between called reaction incubation period, reaction incubation period is longer, the respondents to hide nothing from that truth
• Man was lying, it is difficult to put a lie
flashbacks out, they will lie in according to the order in advance, but never pour to shun again. • 说话缓慢轻柔:表明这个人的内心极度悲伤和焦虑 • Talks slowly gently: indicates that the man's heart is heavy with grief and anxiety.
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Check your answer
• • • • 1.羞愧 3.开心 5.愤怒 7.恐惧 2.不屑 4.悲伤 6.厌恶 8.自然
The voice and manner of speaking • 人在说谎的时候,很难把谎言倒叙出来,他们会预先按顺序
编好谎,但从不会倒着顺一遍言