Jointly Learning the Discriminative Dictionary and Projection for Face Recognition

Chao Bi, Yugen Yi, Lei Zhang, Caixia Zheng, Yanjiao Shi, Xiaochun Xie, Jianzhong Wang and Yan Wu

Mathematical Problems in Engineering, 2020, vol. 2020, 1-17

Abstract:

Recently, dictionary learning has become an active topic. However, the majority of dictionary learning methods directly employs original or predefined handcrafted features to describe the data, which ignores the intrinsic relationship between the dictionary and features. In this study, we present a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn the discriminative dictionary and projection for both image-based and video-based face recognition. The dictionary can realize a tight correspondence between atoms and class labels. Simultaneously, the projection matrix can extract discriminative information from the original samples. Through adopting the Fisher discrimination criterion, the proposed framework enables a better fit between the learned dictionary and projection. With the representation error and coding coefficients, the classification scheme further improves the discriminative ability of our method. An iterative optimization algorithm is proposed, and the convergence is proved mathematically. Extensive experimental results on seven image-based and video-based face databases demonstrate the validity of JLDDP.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1527965

DOI: 10.1155/2020/1527965

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