Face recognition based on regularized nearest points between image sets

In this paper, a novel regularized nearest points (RNP) method is proposed for image sets based face recognition. By modeling an image set as a regularized affine hull (RAH), two regularized nearest points (RNP), one on each image set's RAH, are automatically determined by an efficient iterative solver. The between-set distance of RNP is then defined by considering both the distance between the RNPs and the structure of image sets. Compared with the recently developed sparse approximated nearest points (SANP) method, RNP has a more concise formulation, less variables and lower time complexity. Extensive experiments on benchmark databases (e.g., Honda/UCSD, CMU Mobo and YouTube databases) clearly show that our proposed RNP consistently outperforms state-of-the-art methods in both accuracy and efficiency.

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