Citation

Chong, Siew Chin and Teoh, Andrew Beng Jin and Ong, Thian Song (2017) Unconstrained face verification with a dual-layer block-based metric learning. Multimedia Tools and Applications, 76 (2). pp. 1703-1719. ISSN 1380-7501; eISSN: 1573-7721

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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.

Item Type:	Article
Uncontrolled Keywords:	Unconstrained face, Metric learning, Block-based, Verification, Restricted
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	29 Jan 2018 13:07
Last Modified:	29 Jan 2018 13:07
URII:	http://shdl.mmu.edu.my/id/eprint/6933

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