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 only minimize the reconstruction error of all the face images but also encourage a sub-dictionary to represent the corresponding subject from different videos. Moreover, by introducing the low-rank approximation, the proposed method is able to discover invariant structured information from different videos of the same subject. To this end, an efficient alternating algorithm is employed to learn our structured dictionary. Extensive experiments on three video-based face recognition databases show that our approach outperforms several state-of-the-art methods.

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reconstruction error subdictionary invariant structured information stateoftheart methods invariant structure of face images method videobased face recognition databases invariant structural information structured dictionary learning framework alternating algorithm lowrank approximation discriminative

Zheng, JJ ,Chellappa, R,Xu, HY ,Alavi, A ,.Learning a Structured Dictionary for Video-based Face Recognition. (),9.

Zheng, JJ , et al."Learning a Structured Dictionary for Video-based Face Recognition"

Zheng, JJ ,Chellappa, R,Xu, HY ,Alavi, A ,"Learning a Structured Dictionary for Video-based Face Recognition"