Abstract
Face recognition with single sample per person (SSPP) is a very challenging task because each class lacks sufficient training samples. To address this problem, this paper proposed a new face recognition method called collaborative error propagation (CEP) for single sample face recognition. First, we construct a facial variations dictionary through generic learning set, and rich collaborative representation dictionary. Then, we construct an error function by utilizing the global representation residual error of each testing sample, the error function as soft label influences the following patch classification. Later, partition the entire sample into many overlapping patch, obtain a new patch representation residual combined with the error function. Finally, using all the patch recognition results to get the voting result. Compared with the state of the art single sample face recognition methods, the experimental results demonstrate the efficacy of the proposed method, and shows more robust to complex facial variations, especially for disguise and uneven illumination.
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Acknowledgments
We would like to thank the associate editor and all anonymous reviewers for their constructive comments and suggestions. This research was partially supported by the National Science Foundation of China (Grant No. 61101246) and the Fundamental Research Funds for the Central Universities (Grant No. JB150209).
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Liu, J., Li, L., Li, Q., Wei, X. (2018). Collaborative Error Propagation for Single Sample Face Recognition. In: Peng, Y., Yu, K., Lu, J., Jiang, X. (eds) Intelligence Science and Big Data Engineering. IScIDE 2018. Lecture Notes in Computer Science(), vol 11266. Springer, Cham. https://doi.org/10.1007/978-3-030-02698-1_29
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