Discriminant Analysis via Joint Euler Transform and ℓ2, 1-Norm

Liao, Shuangli and Gao, Quanxue and Yang, Zhaohua and Chen, Fang and Nie, Feiping and Han, Jungong (2018) Discriminant Analysis via Joint Euler Transform and ℓ2, 1-Norm. IEEE Transactions on Image Processing, 27 (11). pp. 5668-5682. ISSN 1057-7149

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Abstract

Linear discriminant analysis (LDA) has been widely used for face recognition. However, when identifying faces in the wild, the existence of outliers that deviate significantly from the rest of the data can arbitrarily skew the desired solution. This usually deteriorates LDA’s performance dramatically, thus preventing it from mass deployment in real-world applications. To handle this problem, we propose an effective distance metric learning method-based LDA, namely, Euler LDA-L21 (e-LDA-L21). e-LDA-L21 is carried out in two stages, in which each image is mapped into a complex space by Euler transform in the first stage and the ℓ2,1 -norm is adopted as the distance metric in the second stage. This not only reveals nonlinear features but also exploits the geometric structure of data. To solve e-LDA-L21 efficiently, we propose an iterative algorithm, which is a closed-form solution at each iteration with convergence guaranteed. Finally, we extend e-LDA-L21 to Euler 2DLDA-L21 (e-2DLDA-L21) which further exploits the spatial information embedded in image pixels. Experimental results on several face databases demonstrate its superiority over the state-of-the-art algorithms.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Image Processing
Additional Information:
©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1712
Subjects:
ID Code:
127700
Deposited By:
Deposited On:
05 Oct 2018 10:46
Refereed?:
Yes
Published?:
Published
Last Modified:
07 Jul 2020 06:21