Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81670
Title: Joint Feature Learning for Face Recognition
Authors: Lu, Jiwen
Liong, Venice Erin
Wang, Gang
Moulin, Pierre
Keywords: Face recognition
Feature learning
Issue Date: 2015
Source: Lu, J., Liong, V. E., Wang, G., & Moulin, P. (2015). Joint Feature Learning for Face Recognition. IEEE Transactions on Information Forensics and Security, 10(7), 1371-1383.
Series/Report no.: IEEE Transactions on Information Forensics and Security
Abstract: This paper presents a new joint feature learning (JFL) approach to automatically learn feature representation from raw pixels for face recognition. Unlike many existing face recognition systems, where conventional feature descriptors, such as local binary patterns and Gabor features, are used for face representation, we propose an unsupervised feature learning method to learn hierarchical feature representation. Since different face regions have different physical characteristics, we propose to use different feature dictionaries to represent them, and to learn multiple yet related feature projection matrices for these regions simultaneously. Hence position-specific discriminative information can be exploited for face representation. Having learned these feature projections for different face regions, we perform spatial pooling for face patches within each region to enhance the representative power of the learned features. Moreover, we stack our JFL model into a deep architecture to exploit hierarchical information for feature representation and further improve the recognition performance. Experimental results on five widely used face data sets show the effectiveness of our proposed approach.
URI: https://hdl.handle.net/10356/81670
http://hdl.handle.net/10220/40924
ISSN: 1556-6013
DOI: 10.1109/TIFS.2015.2408431
Rights: © 2015 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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