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

作者：Yan, Haibin; Lu, Jiwen; Zhou, Xiuzhuang^*; Shang, Yuanyuan

来源：Neurocomputing, 2014, 143: 134-143.

DOI：10.1016/j.neucom.2014.06.012

• Single-sample face recognition
• Multi-feature learning
• Multi-manifold learning

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.

• 出版日期2014-11-2
• 单位首都师范大学