Abstract
In this paper, a novel random facial variation modeling system for sparse representation face recognition is presented. Although recently Sparse Representation-Based Classification (SRC) has represented a breakthrough in the field of face recognition due to its good performance and robustness, there is the critical problem that SRC needs sufficiently large training samples to achieve good performance. To address these issues, we challenge the single-sample face recognition problem with intra-class differences of variation in a facial image model based on random projection and sparse representation. In this paper, we present a developed facial variation modeling systems composed only of various facial variations. We further propose a novel facial random noise dictionary learning method that is invariant to different faces. The experiment results on the AR, Yale B, Extended Yale B, MIT and FEI databases validate that our method leads to substantial improvements, particularly in single-sample face recognition problems.
Highlights
Face recognition has dramatically drawn wide attention due to the advancement of computer vision and pattern recognition technologies [1,2,3]
Inspired by the observation and prior work on sparse representation single sample face recognition [35,36], we present an intra-class facial variation modeling system for sparsity-based Single Sample Per Person (SSPP)
Since our classification algorithm is based on sparse representation-based classification (SRC), we briefly review this algorithm for the sake of clarity
Summary
Face recognition has dramatically drawn wide attention due to the advancement of computer vision and pattern recognition technologies [1,2,3]. Deng et al, applied an auxiliary intra-class variant dictionary to represent the possible variation between the training and testing images [35] They challenge the SSPP problem by proposing a “prototype plus variation” representation dictionary which is assembled by the class centroids and the sample-to-centroid differences for sparsity-based face recognition [36]. Inspired by the observation and prior work on sparse representation single sample face recognition [35,36], we present an intra-class facial variation modeling system for sparsity-based SSPP problem.
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