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Communication dans un congrès

Spontaneous Facial Expression Recognition using Sparse Representation

Abstract : Facial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear combination of prototype atoms via Orthogonal Matching Pursuit algorithm. Sparse codes are then used to train an SVM classifier dedicated to the recognition task. The dictionary that sparsifies the facial images (feature points with the same class labels should have similar sparse codes) is crucial for robust classification. Learning sparsifying dictionaries heavily relies on the initialization process of the dictionary. To improve the performance of dictionaries, a random face feature descriptor based on the Random Projection concept is developed. The effectiveness of the proposed method is evaluated through several experiments on the spontaneous facial expressions DynEmo database. It is also estimated on the well-known acted facial expressions JAFFE database for a purpose of comparison with state-of-the-art methods.
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Contributeur : Dawood Al Chanti <>
Soumis le : mercredi 15 mars 2017 - 20:58:58
Dernière modification le : mercredi 7 octobre 2020 - 11:22:01
Archivage à long terme le : : vendredi 16 juin 2017 - 15:07:35


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Dawood Al Chanti, Alice Caplier. Spontaneous Facial Expression Recognition using Sparse Representation. 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), Feb 2017, Porto, Portugal. pp.11, ⟨10.5220/0006118000640074⟩. ⟨hal-01485279v2⟩



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