关键词: 结构化字典, 分块LBP特征, 综合字典, 分析字典, 判别式字典

Abstract: Aiming at the excessive costs of the solving process of the [l1]-norm sparsity constraint on the representation coefficients, which is used in dictionary learning and classification, a new method is proposed, combining blocking LBP feature and projective dictionary pair learning to recognize the facial expression, at the meantime, to obtain more efficient facial expression-related features for dictionary learning. The blocking LBP feature of the image is extracted to replace the original data sample for training and testing. Then, this paper learns an analysis dictionary and a synthesis dictionary, and the former can obtain the representation coefficient while the latter possesses refactoring capability. Both synthesis dictionary and analysis dictionary are used to calculate the reconstruction error of each category and classify them, and facial expression recognition is achieved consequently. According to the experimental results in the JAFFE and CK+databases, compared with other methods, this proposed method not only can greatly reduce the time complexity of training and testing phases, but also can achieve higher recognition rate in classification tasks.

Key words: structured dictionary, block LBP feature, synthesis dictionary, analysis dictionary, discriminative dictionary