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

This paper presents a Multi-feature Multi-Manifold Learning (M^3L) 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 M^3L 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.