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This study examines the performance of person-independent facial expression recognition improved by adapting the system to a given person. The proposed method transfers the style of particular subjects to the semi-style-free space. There is no need to change the person-independent classifier in order to improve the performance. The style transfer mapping (STM) has been proposed in image-based classification...
We present a bimodal information analysis system for automatic emotion recognition. Our approach is based on the analysis of video sequences which combines facial expressions observed visually with acoustic features to automatically recognize five universal emotion classes: anger, disgust, happiness, sadness and surprise. We address the challenges posed during the temporal analysis of the bimodal...
Presents work on a project for the automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar to how spontaneous speech differs from directed speech. Previous methods for automatic facial expression recognition assumed images were collected in controlled environments in which the subjects deliberately faced the camera. Since...
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