Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.neucom.2014.06.012 | Neurocomputing |
Keywords | DocType | Volume |
multi-feature learning,multi-manifold learning,single-sample face recognition | Journal | 143 |
Issue | ISSN | Citations |
1 | 0925-2312 | 25 |
PageRank | References | Authors |
0.70 | 38 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haibin Yan | 1 | 172 | 8.55 |
| Jiwen Lu | 2 | 3105 | 153.88 |
| Xiuzhuang Zhou | 3 | 380 | 20.26 |
| Yuanyuan Shang | 4 | 210 | 16.83 |