NDLI: Unconstrained face verification with a dual-layer block-based metric learning

Content Provider	Springer Nature Link
Author	Chong, Siew Chin Teoh, Andrew Beng Jin Ong, Thian Song
Copyright Year	2015
Abstract	In this paper, a dual-layer block-based metric learning technique is proposed to better discriminate the face image pairs and accelerate the overall verification process under the unconstrained environment. The input images are processed as blocks to provide a richer base of face features. Our proposed method is formed by two layers, in which the first layer assists in extracting the compact block-based descriptors without the existence of full class label information and to refine the within-class and between-class scatter matrices while the second layer integrates the face descriptors of all blocks. The proposed scheme has computational advantage over the single metric learning method while it exploits the correlations among the multiple metrics from different descriptors. The performance of our proposed method is evaluated on the Labeled Faces in the Wild database and achieves an improved performance when compared with the state-of-the-art methods in terms of verification rate and computation time.
Starting Page	1703
Ending Page	1719
Page Count	17
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	76
Issue Number	2
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2015-12-22
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Unconstrained face Metric learning Block-based Verification Restricted Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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