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Multi-feature multi-manifold learning for single-sample face recognition
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DOI：10.1016/j.neucom.2014.06.012.png)
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This paper presents a Multi-feature Multi-Manifold Learning ((ML)-L-3) 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 (ML)-L-3 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. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Single-sample face recognition
Multi-feature learning
Multi-manifold learning
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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