Abstract
This paper presents a novel method named Consist Sparse Representation (CSR) to solve the problem of video-based face recognition. We treat face images from each set as an ensemble. For each probe set, our goal is that the non-zero elements of the coefficient matrix can ideally focus on the gallery examples from a few/one subject(s). To obtain the sparse representation of a probe set, we simultaneously consider group-sparsity of gallery sets and probe sets. A new matrix norm (i.e. \(l_{F,0}\)-mixed norm) is designed to describe the number of gallery sets selected to represent the probe set. The coefficient matrix is obtained by minimizing the \(l_{F,0}\)-mixed norm which directly counts the number of gallery sets used to represent the probe set. It could better characterize the relations among classes than previous methods based on sparse representation. Meanwhile, a special alternating optimization strategy based on the idea of introducing auxiliary variables is adopted to solve the discontinuous optimization problem. We conduct extensive experiments on Honda, COX and some image set databases. The results demonstrate that our method is more competitive than those state-of-the-art video-based face recognition methods.
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Acknowledgement
The authors would like to thank all the reviewers for their valuable comments. Thanks to Shiguang Shan, Zhen Cui and Ruiping Wang provide the data and code for us. Xiuping Liu is supported by the NSFC Fund (No. 61370143) and NEP Fund (No. f61632006). Junjie Cao is supported by the NSFC Fund (Nos.61363048 and 61262050).
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Liu, X., Shen, A., Zhang, J., Cao, J., Zhou, Y. (2017). Consistent Sparse Representation for Video-Based Face Recognition. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10113. Springer, Cham. https://doi.org/10.1007/978-3-319-54187-7_27
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