Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Machine Analysis of Facial Expressions

No abstract

Facial Mimicry and Social Context Affect Smile Interpretation

Theoretical accounts and extant research suggest that people use various sources of information, including sensorimotor simulation and social context, while judging emotional displays. However, the evidence on how those factors can interplay is limited. The present research tested whether social context information has a greater impact on perceivers’ smile judgments when mimicry is experimentally restricted. In Study 1, participants watched images of affiliative smiles presented with verbal descriptions of situations associated with happiness or politeness. Half the participants could freely move their faces while rating the extent to which the smiles communicated affiliation, whereas for the other half mimicry was restricted via a pen-in-mouth procedure. As predicted, smiles were perceived as more affiliative when the social context was polite than when it was happy. Importantly, the effect of context information was significantly larger among participants who could not freely mimic the facial expressions. In Study 2 we replicated this finding using a different set of stimuli, manipulating context in a within-subjects design, and controlling for empathy and mood. Together, the findings demonstrate that mimicry importantly modulates the impact of social context information on smile perception

Contextual effects on smile perception and recognition memory

Most past research has focused on the role played by social context information in emotion classification, such as whether a display is perceived as belonging to one emotion category or another. The current study aims to investigate whether the effect of context extends to the interpretation of emotion displays, i.e. smiles that could be judged either as posed or spontaneous readouts of underlying positive emotion. A between-subjects design (N = 93) was used to investigate the perception and recall of posed smiles, presented together with a happy or polite social context scenario. Results showed that smiles seen in a happy context were judged as more spontaneous than the same smiles presented in a polite context. Also, smiles were misremembered as having more of the physical attributes (i.e., Duchenne marker) associated with spontaneous enjoyment when they appeared in the happy than polite context condition. Together, these findings indicate that social context information is routinely encoded during emotion perception, thereby shaping the interpretation and recognition memory of facial expressions

Kinematic Characterization of Spontaneous and Posed Facial Expressions of Happiness and Surprise

The relationship between emotions and facial expressions has been largely studied. However, most studies have focused on static and posed facial displays. Research on dynamic spontaneous facial expressions is still needed to understand how humans move their face to genuinely express emotion. Therefore, we conducted a study in which spontaneous and posed facial expressions of six basic emotions (happiness, sadness, anger, fear, disgust, and surprise), were recorded. Spontaneous facial expressions were recorded while participants watched emotion-eliciting videos, specifically selected to elicit the list of target emotions. Posed facial expressions, instead, were recorded while participants were instructed to reproduce a specific facial display while watching static pictures of that display. This thesis consists of an overview about emotion, facial expressions and measuring techniques, and a complete analysis and comparison of facial expressions of happiness and surprise. In particular, we considered the role played by the facial horizontal axis (i.e., the axis dividing the lower and upper parts of the face) in emotion expression. I found different dynamic properties between spontaneous and posed expressions for happiness and surprise. I also found that the upper and lower parts of the face are involved to different degrees in expressions of happiness and surprise. My study provides important evidence to overcome the bias introduced by research that for years has not considered spontaneous expressions or dynamic aspects, and further knowledge that is key for real life applications in clinical, security, and forensic fields.The relationship between emotions and facial expressions has been largely studied. However, most studies have focused on static and posed facial displays. Research on dynamic spontaneous facial expressions is still needed to understand how humans move their face to genuinely express emotion. Therefore, we conducted a study in which spontaneous and posed facial expressions of six basic emotions (happiness, sadness, anger, fear, disgust, and surprise), were recorded. Spontaneous facial expressions were recorded while participants watched emotion-eliciting videos, specifically selected to elicit the list of target emotions. Posed facial expressions, instead, were recorded while participants were instructed to reproduce a specific facial display while watching static pictures of that display. This thesis consists of an overview about emotion, facial expressions and measuring techniques, and a complete analysis and comparison of facial expressions of happiness and surprise. In particular, we considered the role played by the facial horizontal axis (i.e., the axis dividing the lower and upper parts of the face) in emotion expression. I found different dynamic properties between spontaneous and posed expressions for happiness and surprise. I also found that the upper and lower parts of the face are involved to different degrees in expressions of happiness and surprise. My study provides important evidence to overcome the bias introduced by research that for years has not considered spontaneous expressions or dynamic aspects, and further knowledge that is key for real life applications in clinical, security, and forensic fields