Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Sparse temporal representations for facial expression recognition
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Facial expression recognition using geometric and appearance features
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
A multimodal approach for online estimation of subtle facial expression
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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Sparse representation in compressed sensing is a recently developed hot research area in signal processing and artificial intelligence due to its success in various applications. In this paper, a new approach for facial expression recognition (FER) based on fusion of sparse representation is proposed. The new algorithm first solves two sparse representations both on raw gray facial expression images and local binary patterns (LBP) of these images. Then two expression recognition results are obtained on both sparse representations. Finally, the final expression recognition is performed by fusion on the two results. The experiment results on Japanese Female Facial Expression database JAFFE show that the proposed fusion algorithm is much better than the traditional methods such as PCA and LDA algorithms.