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Image set face recognition based on extended low rank recovery and collaborative representation

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Abstract

In the real-world face recognition problems, the collected query set images often suffer serious disturbances. To address the problem, we propose an image set face recognition method based on extended low rank recovery and collaborative representation. By exploiting a Frobenius norm term, an extended low rank representation model is firstly developed to remove all possible disturbances from the query set and reconstruct the rank-one query set. To improve the computational efficiency, a compact and discriminative dictionary is learned from the large gallery set, and the closed form solutions for both the dictionary atom and the coding coefficient are straightway derived. The final classification is performed by using any frame in the reconstructed query set instead of using the whole set, which can further improve the running efficiency. Extensive experiments are conducted on the benchmark Honda/USCD and Youtube Celebrities database to verify that the proposed method outperforms significantly the state-of-the-art methods in terms of robustness and efficiency.

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Acknowledgements

The authors would like to express their sincere gratitude to the anonymous referees, the editor and Ying Li for many valueable suggestions and comments that helped to improve the paper. This work was supported by the National Natural Science Foundation of China (No. 91746107).

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Authors and Affiliations

  1. School of Mathematics, Tianjin University, Tianjin, 300354, China

    Zhanjie Song & Kaiyan Cui

  2. Visual Pattern Analysis Research Lab, Tianjin University, Tianjin, 300072, China

    Zhanjie Song & Guangtao Cheng

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  1. Zhanjie Song
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  2. Kaiyan Cui
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  3. Guangtao Cheng
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Correspondence to Kaiyan Cui.

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Song, Z., Cui, K. & Cheng, G. Image set face recognition based on extended low rank recovery and collaborative representation. Int. J. Mach. Learn. & Cyber. 11, 71–80 (2020). https://doi.org/10.1007/s13042-019-00941-6

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