Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features

2010 ◽  
Vol 20 (2) ◽  
pp. 147-187 ◽  
Author(s):  
Sidney K. D’Mello ◽  
Arthur Graesser
Keyword(s):  
Body Language ◽  
Facial Features ◽  
Conversational Cues ◽  
Affect Detection

scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Body Language ◽  
Facial Features ◽  
Verbal Cues ◽  
The Face ◽  
Tone Of Voice

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

scholarly journals Facial Affect Detection using Transfer Learning: A Comparative Study

2019 ◽  
Author(s):  
Harshil Pandya ◽  
Hardika Patel
Keyword(s):  
Facial Affect ◽  
Machine Learning Algorithms ◽  
Facial Features ◽  
Expression Recognition ◽  
Emotion Detection ◽  
Unseen Data ◽  
Affect Detection ◽  
Value Loss ◽  
Human Faces

Facial affect analysis is perceived as one of themost complex and challenging areas for humanisation ofrobots. Several Facial Expression Recognition (FER)systems apply generic machine learning algorithms toextract facial features. This results in an erroneous classification of previously unseen data. This paper improviseson previous research done on emotion detection and im-plements techniques to leverage the potential of Convolu-tional Neural Networks (CNNs) effectively to classify aset of grayscale images of human faces into seven distinctemotion categories. We experiment with some populartransfer learning models to achieve a maximum accuracyof 98% for the seven-class classification task. To eke outfurther precision and reduce the value loss, we incorpo-rate the Squeeze and Excitation Network to theResNet-50 model, which resulted in a validation accuracyof 99.36%.

КӨРКӨМ АДАБИЯТТАГЫ ПОРТРЕТТИК СҮРӨТТӨӨЛӨРДҮН ТҮРЛӨРҮ, ЫКМАЛАРЫ

2020 ◽  
pp. 31-33
Author(s):  
Алтынай Акылбекова
Keyword(s):  
Social Status ◽  
Body Language ◽  
Facial Features ◽  
Place Of Residence ◽  
Literary Character ◽  
Portrait Painting

Ар бир адам бул личность, ал өзүнө гана тиешелүү сапаттарга, тулку-бойго, тилдик жана кыймыл-аракеттик адаттарга, адамды башкалардан айырмалап турган көз карашка дүйнө таанымга ж.б. ээ. Ошол себептен, адамдын образын чагылдырыш үчүн сүрөткер, аны ар тараптан карап, бардык тараптан баалап тартканга аракет кылат. Анын кийген кийиминен баштап, жүзүндөгү өзгөчөлүк, ой жүгүртүүсү, жашаган жери, социалдык абалы, табиятка болгон мамилеси ж.б. бардыгы кызыктуу, маанилүү. Адабиятта бул өзгөчө көркөм формага ээ болот. Автор адабий каарманды, образдарды мүнөздөп берүү үчүн сөз менен анын портретин түзөт. Макалада көркөм сүрөттөөнүн бир түрү болгон портреттик сүрөттөөлөрдүн принциптери, ыкмалы жөнүндө кеңири сөз болот. Каждый человек - личность, которая зависит только от его собственных качеств, тела, языка и поведенческих привычек, отношений, мировоззрений, отличающих его от других. Поэтому, чтобы отразить образ человека, художник старается посмотреть на него со всех сторон и оценить его со всех сторон. Одежда, черты лица, мышление, место жительства, социальный статус, отношение к природе - все это интересно и важно. В литературе это приобретает особую художественную форму. Автор создает портрет литературного персонажа, описывающийся его словами. В статье рассматриваются принципы и методы портретной живописи как формы художественного описания. Each person is a personality that depends only on his own qualities, body, language and behavioral habits, relationships, worldviews that distinguish him from others. Therefore, in order to reflect the image of a person, the artist tries to look at him from all sides and evaluate him from all sides. Clothes, facial features, thinking, place of residence, social status, attitude to nature - all this is interesting and important. It takes a special artistic form in literature. The author creates a portrait of a literary character describing him in words. The article examines the principles and methods of portrait painting as a form of artistic description.

Can We Detect Cooperators by Looking at Their Face?

2017 ◽  
Vol 26 (3) ◽  
pp. 276-281 ◽  
Author(s):  
Jean-François Bonnefon ◽  
Astrid Hopfensitz ◽  
Wim De Neys
Keyword(s):  
Facial Expressions ◽  
Significant Degree ◽  
Body Language ◽  
Facial Features ◽  
Mutual Benefit ◽  
Economic Games ◽  
Video Clips ◽  
Personal Interactions ◽  
Minimal Information ◽  
Better Than

Humans are willing to cooperate with each other for mutual benefit—and to accept the risk of exploitation. To avoid collaborating with the wrong person, people sometimes attempt to detect cooperativeness in others’ body language, facial features, and facial expressions. But how reliable are these impressions? We review the literature on the detection of cooperativeness in economic games, from those with protocols that provide a lot of information about players (e.g., through long personal interactions) to those with protocols that provide minimal information (e.g., through the presentation of passport-like pictures). This literature suggests that people can detect cooperativeness with a small but significant degree of accuracy when they have interacted with or watched video clips of other players, but that they have a harder time extracting information from pictures. The conditions under which people can detect cooperation from pictures with better than chance accuracy suggest that successful cooperation detection is supported by purely intuitive processes.

scholarly journals A simple multi-feature classification to recognize human emotions in Images

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Facial Expressions ◽  
General Pattern ◽  
Facial Feature ◽  
Body Language ◽  
Facial Features ◽  
Verbal Cues ◽  
The Face ◽  
Tone Of Voice

Emotion recognition by the human brain, normally incorporates context, body language, facial expressions, verbal cues, non-verbal cues, gestures and tone of voice. When considering only the face, piecing together various aspects of each facial feature is critical in identifying the emotion. Since viewing a single facial feature in isolation may result in inaccuracies, this paper attempts training neural networks to first identify specific<br>facial features in isolation, and then use the general pattern of expressions on the face to identify the overall emotion. The reason for classification inaccuracies are also examined.<br>

scholarly journals Affect Detection from Text-Based Virtual Improvisation and Emotional Gesture Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Li Zhang ◽  
Bryan Yap
Keyword(s):  
Gesture Recognition ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Intelligent Agent ◽  
Body Language ◽  
Semantic Interpretation ◽  
Sentence Level ◽  
Affect Detection ◽  
Language Detection ◽  
Initial Exploration

We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The intelligent agent was able to perform sentence-level affect detection from user inputs with strong emotional indicators. However, we noticed that many inputs with weak or no affect indicators also contain emotional implication but were regarded as neutral expressions by the previous interpretation. In this paper, we employ latent semantic analysis to perform topic theme detection and identify target audiences for such inputs. We also discuss how such semantic interpretation of the dialog contexts is used to interpret affect more appropriately during virtual improvisation. Also, in order to build a reliable affect analyser, it is important to detect and combine weak affect indicators from other channels such as body language. Such emotional body language detection also provides a nonintrusive channel to detect users’ experience without interfering with the primary task. Thus, we also make initial exploration on affect detection from several universally accepted emotional gestures.

The Impact of Visual Stereotypes on Judgments about Rape

2016 ◽  
Vol 75 (3) ◽  
pp. 133-140
Author(s):  
Robert Busching ◽  
Johannes Lutz
Keyword(s):  
Mental Representations ◽  
Irrelevant Information ◽  
Clear Distinction ◽  
Rape Myth ◽  
Facial Features ◽  
Reverse Correlation ◽  
Correlation Technique ◽  
Specific Effects ◽  
Legal Judgments ◽  
The Impact

Abstract. Legally irrelevant information like facial features is used to form judgments about rape cases. Using a reverse-correlation technique, it is possible to visualize criminal stereotypes and test whether these representations influence judgments. In the first step, images of the stereotypical faces of a rapist, a thief, and a lifesaver were generated. These images showed a clear distinction between the lifesaver and the two criminal representations, but the criminal representations were rather similar. In the next step, the images were presented together with rape scenarios, and participants (N = 153) indicated the defendant’s level of liability. Participants with high rape myth acceptance scores attributed a lower level of liability to a defendant who resembled a stereotypical lifesaver. However, no specific effects of the image of the stereotypical rapist compared to the stereotypical thief were found. We discuss the findings with respect to the influence of visual stereotypes on legal judgments and the nature of these mental representations.

Face-ing who you are: Do racially prototypic facial features predict racial identification?

2014 ◽  
Author(s):  
Kathy Espino-Perez ◽  
Ryan Folliott ◽  
Brandon K. Brown ◽  
Debbie S. Ma
Keyword(s):  
Facial Features ◽  
Racial Identification
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