Reconnaissance d'états émotionnels par analyse visuelle du visage et apprentissage machine

Abstract : In face-to-face settings, an act of communication includes verbal and emotional expressions. From observation, diagnosis and identification of the individual's emotional state, the interlocutor will undertake actions that would influence the quality of the communication. In this regard, we suggest to improve the way that the individuals perceive their exchanges by proposing to enrich the textual computer-mediated communication by emotions felt by the collaborators. To do this, we propose to integrate a real time emotions recognition system in a platform “Moodle”, to extract them from the analysis of facial expressions of the distant learner in collaborative activities. There are three steps to recognize facial expressions. First, the face and its components (eyebrows, nose, mouth, eyes) are detected from the configuration of facial landmarks. Second, a combination of heterogeneous descriptors is used to extract the facial features. Finally, a classifier is applied to classify these features into six predefined emotions as well as the neutral state. The performance of the proposed system will be assessed on a public basis of posed and spontaneous facial expressions such as Cohn-Kanade (CK), Karolinska Directed Emotional Faces (KDEF) and Facial Expressions and Emotion Database (FEED).
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Khadija Lekdioui. Reconnaissance d'états émotionnels par analyse visuelle du visage et apprentissage machine. Synthèse d'image et réalité virtuelle [cs.GR]. Université Bourgogne Franche-Comté; Université Ibn Tofail. Faculté des sciences de Kénitra, 2018. Français. ⟨NNT : 2018UBFCA042⟩. ⟨tel-02077681⟩

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