Locality regularization embedding for face verification

Pang, Ying Han and Teoh, Andrew Ben Jin and Hiew, Fu San (2015) Locality regularization embedding for face verification. Pattern Recognition, 48 (1). pp. 86-102. ISSN 0031-3203

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Abstract

Graph embedding (GE) is a unified framework for dimensionality reduction techniques. GE attempts to maximally preserve data locality after embedding for face representation and classification. However, estimation of true data locality could be severely biased due to limited number of training samples, which trigger overfitting problem. In this paper, a graph embedding regularization technique is proposed to remedy this problem. The regularization model, dubbed as Locality Regularization Embedding (LRE), adopts local Laplacian matrix to restore true data locality. Based on LRE model, three dimensionality reduction techniques are proposed. Experimental results on five public benchmark face datasets such as CMU PIE, FERET, ORL, Yale and FRGC, along with Nemenyi Post-hoc statistical of significant test attest the promising performance of the proposed techniques.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 06 Nov 2014 04:26
Last Modified: 05 Jan 2017 04:10
URI: http://shdl.mmu.edu.my/id/eprint/5646

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