Coupled Discriminative Feature Learning for Heterogeneous Face Recognition

This paper presents a coupled discriminative feature learning (CDFL) method for heterogeneous face recognition (HFR). Different from most existing HFR approaches which use hand-crafted feature descriptors for face representation, our CDFL directly learns discriminative features from raw pixels for f...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security 2015-03, Vol.10 (3), p.640-652
Main Authors: Jin, Yi, Lu, Jiwen, Ruan, Qiuqi
Format: Article
Language:English
Subjects:
Correlation
coupled learning
discriminative learning
Eigenvalues and eigenfunctions
Face
Face recognition
Feature extraction
Heterogeneous face recognition
Nickel
Vectors
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Summary:This paper presents a coupled discriminative feature learning (CDFL) method for heterogeneous face recognition (HFR). Different from most existing HFR approaches which use hand-crafted feature descriptors for face representation, our CDFL directly learns discriminative features from raw pixels for face representation. In particular, a couple of image filters is learned in CDFL to simultaneously exploit discriminative information and to reduce the appearance difference of face images captured across different modalities. With the help of the learned filters, CDFL can maximize the interclass variations and minimize the intraclass variations of the learned feature vectors, and meanwhile maximize the correlation of face images of the same person from different modalities by solving a generalized eigenvalue problem. Experimental results on three different heterogeneous face recognition applications show the effectiveness of our proposed approach.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2015.2390414