Efficient Misalignment-Robust Representation for Real-Time Face Recognition

Abstract

Abstract. Sparse representation techniques for robust face recognition have been widely studied in the past several years. Recently face recognition with simultaneous misalignment, occlusion and other variations has achieved interesting results via robust alignment by sparse representation (RASR). In RASR, the best alignment of a testing sample is sought subject by subject in the database. However, such an exhaustive search strategy can make the time complexity of RASR prohibitive in large-scale face databases. In this paper, we propose a novel scheme, namely misalignment robust representation (MRR), by representing the misaligned testing sample in the transformed face space spanned by all subjects. The MRR seeks the best alignment via a two-step optimization with a coarse-to-fine search strategy, which needs only two deformation-recovery operations. Extensive experiments on representative face databases show that MRR has almost the same accuracy as RASR in various face recognition and verification tasks but it runs tens to hundreds of times faster than RASR. The running time of MRR is less than 1 second in the large-scale Multi-PIE face database, demonstrating its great potential for real-time face recognition.

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

“…Most work in the literature uses multiple training samples per subject. However, an emerging tendency in FR is to use Single Training Sample per Subject (STSS) [25][26] [27], which is a more challenging problem. By applying such strategy to the images from the databases indicated above, we obtain a similarity matrix of 38x38 comparisons for Extended YALE B, 123 x123 comparisons for Extended Cohn Kanade (CK+), 152 x152 comparisons for FRGC v1, 200 x200 comparisons for FEI and 50 x50 comparisons for Georgia Tech, which significantly reduce the computational complexity of the algorithm compared to traditional multiple training samples per subject.…”

Single-Sample Face Recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

“…For the alignment benchmark, we compare with the deformable SRC (DSRC) algorithm [24] and the misalignment robust representation (MRR) algorithm [30].…”

“…In [24], in order to guarantee the training images contain sufficient illumination patterns, the test subjects must further go through a nontrivial passport-style image collection process in a dark room in order to be entered into the training database. More recently, another development in the SRC framework is simultaneous face alignment and recognition methods [28,15,30]. Nevertheless, these methods did not go beyond the basic assumption used in SRC and other prior art that the face illumination model is measured by a plurality of training samples for each class.…”

Double layers sparse representation for occluded face recognition

“…To solve this problem, Meng Yang etc proposed to solve the problem in paper [4] by extending training samples using the difference between samples. And Paper [5] uses the image Gabor-features for SRC, which can get more compact occlusion dictionary. As a result, the computation complexity and the number of atoms are reduced.…”