Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition

Guo, Yuwei and Jiao, Licheng and Wang, Shuang and Wang, Shuo and Liu, Fang (2017) Fuzzy Sparse Autoencoder Framework for Single Image Per Person Face Recognition. IEEE Transactions on Cybernetics, 48 (8). pp. 2402-2415. ISSN 2168-2267

[img]
Preview
Text
Fuzzy sparse autoencoder framework for single image per person face recognition.pdf - Accepted Version

Download (8MB)

Abstract

The issue of single sample per person (SSPP) face recognition has attracted more and more attention in recent years. Patch/local-based algorithm is one of the most popular categories to address the issue, as patch/local features are robust to face image variations. However, the global discriminative information is ignored in patch/local-based algorithm, which is crucial to recognize the nondiscriminative region of face images. To make the best of the advantage of both local information and global information, a novel two-layer local-to-global feature learning framework is proposed to address SSPP face recognition. In the first layer, the objective-oriented local features are learned by a patch-based fuzzy rough set feature selection strategy. The obtained local features are not only robust to the image variations, but also usable to preserve the discrimination ability of original patches. Global structural information is extracted from local features by a sparse autoencoder in the second layer, which reduces the negative effect of nondiscriminative regions. Besides, the proposed framework is a shallow network, which avoids the over-fitting caused by using multilayer network to address SSPP problem. The experimental results have shown that the proposed local-to-global feature learning framework can achieve superior performance than other state-of-the-art feature learning algorithms for SSPP face recognition.

Item Type: Article
Additional Information: © 2017 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: two-layer feature learning, fuzzy rough set, sparse autoencoder, one sample per person face recognition
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
REF UoA Output Collections > REF2021 UoA11: Computer Science and Informatics
Depositing User: Shuo Wang
Date Deposited: 12 Jul 2019 06:32
Last Modified: 12 Jul 2019 08:59
URI: http://www.open-access.bcu.ac.uk/id/eprint/7723

Actions (login required)

View Item View Item

Research

In this section...