Adaptive linear regression for single-sample face recognition

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• 作者：Biao Wang^a ; ^b ; ^c ; ^{wangbiao.thu@gmail.com} ; ^{wangbiao08@mails.thu.edu.cn}Author Vitae ; Weifeng Li^a ; ^b ; ^c ; ^{li.weifeng@sz.tsinghua.edu.cn}Author Vitae ; Zhimin Li^d ; Qingmin Liao^a ; ^b ; ^c ; ^{liaoqm@tsinghua.edu.cn}Author Vitae
• 关键词：Face recognition ; Single sample per person (SSPP) ; Linear regression
• 刊名：Neurocomputing
• 出版年：2013
• 出版时间：4 September, 2013
• 年：2013
• 卷：115
• 期：Complete
• 页码：186-191
• 全文大小：916 K

文摘

The single sample per person problem (SSPP) is quite common in real-world face recognition applications. In such circumstance, the lack of enough training samples often results in poor generalization ability for majority of the existing state-of-the-art methods. To address this problem, in this paper, a fairly simple but effective approach, called adaptive linear regression classifier (ALRC), is presented based on the simple observation that similar subjects have similar intra-personal variations. ALRC is a linear model representing a probe image as a linear combination of the single class-specific gallery and the intra-personal variations adaptively pulled from his/her kNNs in an auxiliary generic training set with multiple samples per person. ALRC can be easily employed with a regularized least square estimator and the decision is ruled in favor of the class with the minimum reconstruction error. Experimental results on AR and FERET face datasets show that ALRC outperforms several state-of-the-art approaches and demonstrates promising abilities against variations including expression, illumination and disguise.