Multi-feature multi-manifold learning for single-sample face recognition

• Published in: Neurocomputing (Amsterdam) 2014-11, Vol.143, p.134-143
• Main Authors: Yan, Haibin, Lu, Jiwen, Zhou, Xiuzhuang, Shang, Yuanyuan
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Data processing. List processing. Character string processing; Exact sciences and technology; Memory organisation. Data processing; Multi-feature learning; Multi-manifold learning; Pattern recognition. Digital image processing. Computational geometry; Single-sample face recognition; Software
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.06.012

This paper presents a Multi-feature Multi-Manifold Learning (M3L) method for single-sample face recognition (SSFR). While numerous face recognition methods have been proposed over the past two decades, most of them suffer a heavy performance drop or even fail to work for the SSFR problem because there are not enough training samples for discriminative feature extraction. In this paper, we propose a M3L method to extract multiple discriminative features from face image patches. First, each registered face image is partitioned into several non-overlapping patches and multiple local features are extracted within each patch. Then, we formulate SSFR as a multi-feature multi-manifold matching problem and multiple discriminative feature subspaces are jointly learned to maximize the manifold margins of different persons, so that person-specific discriminative information is exploited for recognition. Lastly, we present a multi-feature manifold–manifold distance measure to recognize the probe subjects. Experimental results on the widely used AR, FERET and LFW datasets demonstrate the efficacy of our proposed approach.