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Facial expression analysis happiness subtleties

Which facial features allow human observers to successfully recognize expressions of emotion? While the eyes and mouth have been frequently shown to be of high importance, research on facial action units has made more precise predictions about the areas involved in displaying each emotion. The present research investigated on a fine-grained level, which physical features are most relied on when decoding facial expressions. In the experiment, individual faces expressing the basic emotions according to Ekman were hidden behind a mask of 48 tiles, which was sequentially uncovered. Participants were instructed to stop the sequence as soon as they recognized the facial expression and assign it the correct label. For each part of the face, its contribution to successful recognition was computed, allowing to visualize the importance of different face areas for each expression.
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Facial Expression Analysis: The Complete Pocket Guide

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Automated Video Based Facial Expression Analysis of Neuropsychiatric Disorders

In the wake of rapid advances in automatic affect analysis, commercial automatic classifiers for facial affect recognition have attracted considerable attention in recent years. While several options now exist to analyze dynamic video data, less is known about the relative performance of these classifiers, in particular when facial expressions are spontaneous rather than posed. In the present work, we tested eight out-of-the-box automatic classifiers, and compared their emotion recognition performance to that of human observers. A total of videos were sampled from two large databases that conveyed the basic six emotions happiness, sadness, anger, fear, surprise, and disgust either in posed BU-4DFE or spontaneous UT-Dallas form. Results revealed a recognition advantage for human observers over automatic classification. Subsequent analyses per type of expression revealed that performance by the two best performing classifiers approximated those of human observers, suggesting high agreement for posed expressions. However, classification accuracy was consistently lower although above chance level for spontaneous affective behavior.
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The Child Emotion Facial Expression Set: A Database for Emotion Recognition in Children

Emotions are the essence of what makes us human. They impact our daily routines, our social interactions, our attention, perception, and memory. One of the strongest indicators for emotions is our face. Computer-based facial expression recognition mimics our human coding skills quite impressively as it captures raw, unfiltered emotional responses towards any type of emotionally engaging content. But how exactly does it work?
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Background: This study developed a photo and video database of 4-toyear-olds expressing the seven induced and posed universal emotions and a neutral expression. Children participated in photo and video sessions designed to elicit the emotions, and the resulting images were further assessed by independent judges in two rounds. Methods: In the first round, two independent judges 1 and 2 , experts in the Facial Action Coding System, firstly analysed 3, emotions facial expressions stimuli from children.
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