Abstract
With the progress and development of mobile camera and video surveillance, it becomes more efficiently to collect multiple face images for each query. Face recognition based on image set has attracted more and more attention in the community of computer vision and the application of biometrics. In this paper, instead of using handcraft features, we proposed to utilize the deep feature (e.g., convolutional neural network feature) in the application of image-set face recognition. In order to fully explore the discrimination of original query samples and the query virtual nearest point, we proposed a novel joint collaborative representation with a newly designed class-level similarity constraint on the coding coefficients. An alternative solving algorithm is proposed to solve the proposed model. Two experiments were conducted on the YouTube Face database and a new image-set database established based on Labeled Faced in the Wild (LFW). The result of experiments show that our approach has more advantages than previous image-set face recognition approaches.
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Acknowledgment
This work is partially supported by the National Natural Science Foundation for Young Scientists of China (no. 61402289), and National Science Foundation of Guang-dong Province (no. 2014A030313558).
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Li, H., Yang, M. (2017). Joint Collaborative Representation with Deep Feature for Image-Set Face Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_19
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DOI: https://doi.org/10.1007/978-3-319-69923-3_19
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