Emotion perception in emotionless face images suggests a norm-based representation

Neth, Donald; Martı́nez, Aleix M.

doi:10.1167/9.1.5

Cited by 71 publications

(71 citation statements)

References 32 publications

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“…By contrast, highly affiliative neutral faces look happy. Put another way, the facial configurations that create impressions of dominance and affiliation are the same that make a face appear to show anger and happiness in line with the findings of Neth & Martinez (2009). These perceptual similarities between dominance/anger and affiliation/happiness then can be expected to bias the perception of these emotions, especially when facial expressions are weak and ambiguous.…”

Section: Pattern Matchingsupporting

confidence: 60%

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The face is not an empty canvas: how facial expressions interact with facial appearance

Heß

Adams

Kleck

2009

Phil. Trans. R. Soc. B

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Faces are not simply blank canvases upon which facial expressions write their emotional messages. In fact, facial appearance and facial movement are both important social signalling systems in their own right. We here provide multiple lines of evidence for the notion that the social signals derived from facial appearance on the one hand and facial movement on the other interact in a complex manner, sometimes reinforcing and sometimes contradicting one another. Faces provide information on who a person is. Sex, age, ethnicity, personality and other characteristics that can define a person and the social group the person belongs to can all be derived from the face alone. The present article argues that faces interact with the perception of emotion expressions because this information informs a decoder's expectations regarding an expresser's probable emotional reactions. Facial appearance also interacts more directly with the interpretation of facial movement because some of the features that are used to derive personality or sex information are also features that closely resemble certain emotional expressions, thereby enhancing or diluting the perceived strength of particular expressions.Keywords: emotion; facial expression; facial appearance Facial expressions of emotion have long been of interest to philosophers and psychologists. Darwin's (1872Darwin's ( / 1965 seminal work On the Expression of the Emotions in Man and Animals was a first attempt to systematically understand emotion expression and its meaning. In this book, he proposed a number of explanations as to why certain facial and bodily behaviours communicate certain emotions. In doing so, Darwin had no doubt that the expressive behaviour that he described was part of an underlying emotional state, that is, that emotion expressions have communicative value specifically because they are outward manifestations of an inner state. Darwin assumed clear parallels and antecedents to human emotions in the emotions of animals and our humanoid ancestors, and consequently considered emotion expressions to be universal across human cultures.

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Section: Pattern Matchingsupporting

confidence: 60%

“…Further, certain facial configurations make neutral faces appear to show emotions. Thus, a shorter distance between the eyes and mouth, more typical for male faces, leads to the perception of an angrier face (Neth & Martinez 2009). …”

Section: Two Strategies For the Recognition Of Emotionmentioning

confidence: 99%

The face is not an empty canvas: how facial expressions interact with facial appearance

Heß

Adams

Kleck

2009

Phil. Trans. R. Soc. B

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“…Experiments with human subjects demonstrate that reflectance, albedo, and shape play a role in the recognition of the emotion class from face images, with an emphasis on the latter (20,(27)(28)(29). Our face space will hence be given by shape features and Gabor filter responses.…”

Section: Significancementioning

confidence: 99%

“…With 94 fiducials, we have 2 · ð94 · 93Þ=2 = 8; 742 features (dimensions) defining the shape of the face. These interfiducial relative positions are known as configural (or second-order) features and are powerful categorizers of emotive faces (28).…”

Section: Significancementioning

confidence: 99%

Compound facial expressions of emotion

Tao

Martı́nez

2014

Proc. Natl. Acad. Sci. U.S.A.

638

467

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Understanding the different categories of facial expressions of emotion regularly used by us is essential to gain insights into human cognition and affect as well as for the design of computational models and perceptual interfaces. Past research on facial expressions of emotion has focused on the study of six basic categories-happiness, surprise, anger, sadness, fear, and disgust. However, many more facial expressions of emotion exist and are used regularly by humans. This paper describes an important group of expressions, which we call compound emotion categories. Compound emotions are those that can be constructed by combining basic component categories to create new ones. For instance, happily surprised and angrily surprised are two distinct compound emotion categories. The present work defines 21 distinct emotion categories. Sample images of their facial expressions were collected from 230 human subjects. A Facial Action Coding System analysis shows the production of these 21 categories is different but consistent with the subordinate categories they represent (e.g., a happily surprised expression combines muscle movements observed in happiness and surprised). We show that these differences are sufficient to distinguish between the 21 defined categories. We then use a computational model of face perception to demonstrate that most of these categories are also visually discriminable from one another.

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“…Since face-to-face communications between humans involve emotions and what they convey [15], facial expressions are also important in advanced human-machine interfaces. The Facial Action Coding System or FACS [4] is probably the most well-known study about the coding of facial actions.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Facial Expressions by Cortical Multi-scale Line and Edge Coding

Sousa

Rodrigues

Buf

2010

Lecture Notes in Computer Science

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Abstract. Face-to-face communications between humans involve emotions, which often are unconsciously conveyed by facial expressions and body gestures. Intelligent human-machine interfaces, for example in cognitive robotics, need to recognize emotions. This paper addresses facial expressions and their neural correlates on the basis of a model of the visual cortex: the multi-scale line and edge coding. The recognition model links the cortical representation with Paul Ekman's Action Units which are related to the different facial muscles. The model applies a top-down categorization with trends and magnitudes of displacements of the mouth and eyebrows based on expected displacements relative to a neutral expression. The happy vs. not-happy categorization yielded a correct recognition rate of 91%, whereas final recognition of the six expressions happy, anger, disgust, fear, sadness and surprise resulted in a rate of 78%.

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Emotion perception in emotionless face images suggests a norm-based representation

Cited by 71 publications

References 32 publications

The face is not an empty canvas: how facial expressions interact with facial appearance

The face is not an empty canvas: how facial expressions interact with facial appearance

Compound facial expressions of emotion

Recognition of Facial Expressions by Cortical Multi-scale Line and Edge Coding

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