Shearlet Network-based Sparse Coding Augmented by Facial Texture Features for Face Recognition

One open challenge in face recognition (FR) is the single training sample per subject. This paper addresses this problem through a novel approach called Shearlet Network (SN) which takes advantage of the sparse representation (SR) properties of shearlets in biometric applications, specifically, for face coding and recognition. Shearlets are derived from wavelets with composite dilations, a method extending the traditional wavelet approach by allowing for the construction of waveforms defined not only at various scales and locations but also at various orientations. The contributions of this paper are the combination of the power of multi-scale representation with a unique ability to capture geometric information to derive a very efficient representation of facial templates, and the use of a PCA-based approach to design a fusion step by a refined model of belief function based on the Dempster-Shafer rule in the context of confusion matrices. This last step is helpful to improve the processing of facial texture features. We compared our new algorithm (SNPCA) against SN, a wavelet network (WN) implementation and other standard algorithms. Our tests, run on several face databases including FRGC, Extended Yale B database and others, show that this approach yields a very competitive performance as compared to wavelet networks (WN), standard shearlet and PCA-based methods.