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In this paper, we propose a novel regularized sparse coding approach for template-based unconstrained face verification. Unlike traditional verification tasks, which require the evaluation on image-to-image or video-to-video pairs, template-based face verification/recognition methods can exploit training and/or gallery data containing a mixture of both images or videos from the person of interest...
In this paper, we propose a structured dictionary learning framework for video-based face recognition. We discover the invariant structural information from different videos of each subject. Specifically, we employ dictionary learning and low-rank approximation to preserve the invariant structure of face images in videos. The learned dictionary is both discriminative and reconstructive. Thus, we not...
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