Holistic gaze strategy to categorize facial expression of varying intensities

Using faces representing exaggerated emotional expressions, recent behaviour and eye-tracking studies have suggested a dominant role of individual facial features in transmitting diagnostic cues for decoding facial expressions. Considering that in everyday life we frequently view low-intensity expressive faces in which local facial cues are more ambiguous, we probably need to combine expressive cues from more than one facial feature to reliably decode naturalistic facial affects. In this study we applied a morphing technique to systematically vary intensities of six basic facial expressions of emotion, and employed a self-paced expression categorization task to measure participants’ categorization performance and associated gaze patterns. The analysis of pooled data from all expressions showed that increasing expression intensity would improve categorization accuracy, shorten reaction time and reduce number of fixations directed at faces. The proportion of fixations and viewing time directed at internal facial features (eyes, nose and mouth region), however, was not affected by varying levels of intensity. Further comparison between individual facial expressions revealed that although proportional gaze allocation at individual facial features was quantitatively modulated by the viewed expressions, the overall gaze distribution in face viewing was qualitatively similar across different facial expressions and different intensities. It seems that we adopt a holistic viewing strategy to extract expressive cues from all internal facial features in processing of naturalistic facial expressions

Facial Expression Recognition from World Wild Web

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%

Orientation Information in Encoding Facial Expressions for People With Central Vision Loss.

Purpose:Patients with central vision loss face daily challenges on performing various visual tasks. Categorizing facial expressions is one of the essential daily activities. The knowledge of what visual information is crucial for facial expression categorization is important to the understanding of the functional performance of these patients. Here we asked how the performance for categorizing facial expressions depends on the spatial information along different orientations for patients with central vision loss. Methods:Eight observers with central vision loss and five age-matched normally sighted observers categorized face images into four expressions: angry, fearful, happy, and sad. An orientation filter (bandwidth = 23°) was applied to restrict the spatial information within the face images, with the center of the filter ranged from horizontal (0°) to 150° in steps of 30°. Face images without filtering were also tested. Results:When the stimulus visibility was matched, observers with central vision loss categorized facial expressions just as well as their normally sighted counterparts, and showed similar confusion and bias patterns. For all four expressions, performance (normalized d'), uncorrelated with any of the observers' visual characteristics, peaked between -30° and 30° filter orientations and declined systematically as the filter orientation approached vertical (90°). Like normally sighted observers, observers with central vision loss also relied mainly on mouth and eye regions to categorize facial expressions. Conclusions:Similar to people with normal vision, people with central vision loss rely primarily on the spatial information around the horizontal orientation, in particular the regions around the mouth and eyes, for recognizing facial expressions

Do Humans Prefer Faces? Zygomatic Muscle Responses to Neutral Faces vs. Neutral Objects

The present study examined the significance of viewing images of neutral faces versus images of neutral objects on zygomatic muscle activity using facial EMG. Participants (60% women) from a pool of introductory psychology courses had their facial EMG recordings measured in response to images of neutral faces and neutral objects. Participants’ valence rating of each image was also recorded using the Self-Assessment Manikin (SAM) in order to rate their emotional response to each image. The primary hypothesis was that participants would have greater activity in the zygomatic muscle region when presented with images of neutral faces as opposed to lessor activity when presented with images of neutral objects. It was also hypothesized that if participants preferred seeing images of faces as compared to objects, their positive feelings would produce higher SAM ratings. Results from the present study indicated images of neutral faces showed no significant difference in EMG activity compared to images of neutral objects. Self-report data also showed no significant difference in pleasantness or emotional valence between ratings of neutral faces and ratings of neutral objects

What does the amygdala contribute to social cognition?

The amygdala has received intense recent attention from neuroscientists investigating its function at the molecular, cellular, systems, cognitive, and clinical level. It clearly contributes to processing emotionally and socially relevant information, yet a unifying description and computational account have been lacking. The difficulty of tying together the various studies stems in part from the sheer diversity of approaches and species studied, in part from the amygdala's inherent heterogeneity in terms of its component nuclei, and in part because different investigators have simply been interested in different topics. Yet, a synthesis now seems close at hand in combining new results from social neuroscience with data from neuroeconomics and reward learning. The amygdala processes a psychological stimulus dimension related to saliency or relevance; mechanisms have been identified to link it to processing unpredictability; and insights from reward learning have situated it within a network of structures that include the prefrontal cortex and the ventral striatum in processing the current value of stimuli. These aspects help to clarify the amygdala's contributions to recognizing emotion from faces, to social behavior toward conspecifics, and to reward learning and instrumental behavior