A paired sparse representation model for robust face recognition from a single sample. (April 2020)

• Record Type: Journal Article
• Authors: Mokhayeri, Fania; Granger, Eric
• Is Part Of: Pattern recognition. Volume 100(2020:Apr.)
• Publication Date: 2020-04
• Subjects: Face recognition -- Sparse representation-based classification -- Face synthesis -- Generic learning -- Simultaneous sparsity -- Video surveillance; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.107129
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A paired sparse representation model for still-to-video face recognition, using generic learning and data augmentation to represent both linear and non-linear variations based on a single reference still ROI. A simultaneous optimization technique is proposed to encourage the augmented gallery dictionary to share the same sparsity pattern with a similar pose angle in the auxiliary dictionary. A method to design a compact augmented dictionary using row sparsity for SRC which reduce the time and memory complexity. Abstract: Sparse representation-based classification (SRC) has been shown to achieve a high level of accuracy in face recognition (FR). However, matching faces captured in unconstrained video against a gallery with a single reference facial still per individual typically yields low accuracy. For improved robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary dictionary. Despite their success in handling linear variations, non-linear variations (e.g., pose and expressions) between probe and reference facial images cannot be accurately reconstructed with a linear combination of images in the gallery and auxiliary dictionaries because they do not share the same type of variations. In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images. The proposed model, called synthetic plus variational model, reconstructs a probe image by jointly using (1) a variational dictionary and (2) a gallery dictionary augmented with a set of synthetic images generated over a wide diversity of pose angles. The augmented gallery dictionary is then encouraged to pair the same sparsity pattern with the variational dictionary for similar pose angles by solving a newly formulated simultaneous sparsity-based optimization problem. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a single sample per person.