Adaptive joint block-weighted collaborative representation for facial expression recognition


Abstract: Facial expression recognition (FER) plays a significant role in human-computer interactions. Recently, regularized linear representation-based classification has achieved satisfying results in FER. Considering that different blocks in a sample should contribute differently to the representation and classification, we propose an adaptive joint block-weighted collaborative representation-based classification (JBW_CRC) method to effectively exploit the similarity and distinctiveness of different blocks. In JBW_CRC, samples are divided into different blocks and each block of the query sample is represented as a feature vector. Each feature vector is coded on its related block dictionary, which considers the similarity among the feature vectors. Additionally, the distinctiveness of different feature vectors is obtained by weighting its distance to other features, which addresses the distinctiveness in the different feature vectors. The proposed method is verified from the aspect of training samples, time complexity, and Gaussian noise variances on benchmark databases and the extensive experiments show that the proposed method is very competitive with some similar pattern classification methods.

Keywords: Facial expression recognition, block-weighted collaborative representation, similarity, distinctiveness,

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