Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4970366 | Signal Processing: Image Communication | 2018 | 44 Pages |
Abstract
Sparse representation based classification shows significant performance on face recognition (FR) when there are enough available training samples per subject. However, FR often suffers from insufficient training samples. To tackle this problem, a novel classification technique is presented based on utilizing existing available samples rather than constructing auxiliary training samples. An inverse projection-based pseudo-full-space representation (PFSR) is firstly proposed to stably and effectively exploit complementary information between samples. The representation ability of sparse representation-based methods is quantified by defining category concentration index. In order to match PFSR and complete classification, a simple classification criterion, category contribution rate, is designed. Extensive experimentations on the AR, Extended Yale B and CMU Multi-PIE databases demonstrate that PFSR-based classification method is competitive and robust for insufficient training samples FR problem.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Xiaohui Yang, Fang Liu, Li Tian, Haifei Li, Xiaoying Jiang,