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This paper describes emotion recognition system based on facial expression. A fully automatic facial expression recognition system is based on three steps: face detection, facial characteristic extraction and facial expression classification. We have developed an anthropometric model to detect facial feature points combined to Shi&amp, Thomasi(More)
A probabilistic approach has been developed to extract recurrent serious Occupational Accident with Movement Disturbance (OAMD) scenarios from narrative texts within a prevention framework. Relevant data extracted from 143 accounts was initially coded as logical combinations of generic accident factors. A Bayesian Network (BN)-based model was then built for(More)
The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT)(More)
In this paper, we present a detection and tracking feature points algorithm in real time camera input environment. To trace and extract a face image, we use a modified face detector based on the Haar-like features. For feature points detection, we use good features to track of Shi and Thomasi. In order to track the facial feature points, Pyramidal(More)
This paper presents an automatic approach for emotion recognition from a bimodal system based on facial expressions and physiological signals. The information fusion is to combine information from both modalities. We tested two approaches, one based on mutual information which allows the selection of relevant information, the second approach is based on(More)
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