Abstract
In this paper, a novel class-specific kernel linear regression classification is proposed for face recognition under very low-resolution and severe illumination variation conditions. Since the low-resolution problem coupled with illumination variations makes ill-posed data distribution, the nonlinear projection rendered by a kernel function would enhance the modeling capability of linear regression for the ill-posed data distribution. The explicit knowledge of the nonlinear mapping function can be avoided by using the kernel trick. To reduce nonlinear redundancy, the low-rank-r approximation is suggested to make the kernel projection be feasible for classification. With the proposed class-specific kernel projection combined with linear regression classification, the class label can be determined by calculating the minimum projection error. Experiments on 8 × 8 and 8 × 6 images down-sampled from extended Yale B, FERET, and AR facial databases revealed that the proposed algorithm outperforms the state-of-the-art methods under severe illumination variation and very low-resolution conditions.
Highlights
Numerous studies [1] have been greatly proposed for face recognition recently
Simulations carried on the extended Yale B, FERET, and AR facial databases reveal that the proposed kernel linear regression classification (KLRC) can achieve good performance for LR face recognition under variable lighting changes without any preprocessing
5 Experimental results For verifications, we examine the proposed algorithms on the facial images, which are down-sampled from the extended Yale B (EYB) [41], AR [42], and FERET [43] face databases
Summary
Numerous studies [1] have been greatly proposed for face recognition recently. In realistic situations such as video surveillance, face recognition may encounter many great challenges, especially low-resolution problems, caused by the cameras at a distance. Other methods including the gradient operation, Gabor filters, and LDA-based approaches are well-known illumination invariant methods These methods would fail because the important features in high-frequency details for face recognition are lost under the LR problems. 1.2 Contributions We propose a novel face recognition algorithm to improve the limitation of the LRC [15] by embedding the kernel method into the linear regression. Simulations carried on the extended Yale B, FERET, and AR facial databases reveal that the proposed kernel linear regression classification (KLRC) can achieve good performance for LR face recognition under variable lighting changes without any preprocessing. The proposed algorithm can reconstruct the very low-resolution face image under illumination variations with high quality measured by quality assessments.
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