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.
Highlights
Face recognition (FR) is an imperative issue in the field of image processing and computer vision
Our jointly learning the discriminative dictionary and projection (JLDDP) combines the processes of feature projection and dictionary learning into a unified framework to obtain a more suitable low-dimensional feature, which is quite different from RDCDL
In [54], a new sparse representation-based alignment method is proposed for real-world images, which can eliminate the variety of orientations, expressions, and other factors as much as possible
Summary
Face recognition (FR) is an imperative issue in the field of image processing and computer vision. Xu et al [29] proposed a method to learn a structured dictionary for video-based face recognition, which adopted the nuclear norm to make the coding coefficient matrix be low-rank This method did not enhance the discriminative ability of the representation coefficients. In [40], the sigmoid function and the ratio of intraclass representation error to interclass representation error were utilized to learn the discriminative dictionary and projection simultaneously, but it ignored both the intraclass and interclass scatter matrix of the coefficients and low-dimensional samples To address this problem, Feng et al [41] introduced an orthogonal projection matrix, which can be obtained through maximizing the total scatter and betweenclass scatter of the training set, in the projection and dictionary simultaneously learning framework.
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