Whole space subclass discriminant analysis for face recognition

Mandal, Bappaditya; Li, Liyuan; Chandrasekhar, Vijay; Lim, Joo Hwee

Abstract

In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analysis on within-class and within-subclass eigen-spectrums of face images and propose a novel method of eigen-spectrum modeling which extracts discriminative features of faces from both within-subclass and total or between-subclass scatter matrices. Effective low-dimensional face discriminative features are extracted for face recognition (FR) after performing discriminant evaluation in the entire eigenspace. Experimental results on popular face databases (AR, FERET) and the challenging unconstrained YouTube Face database show the superiority of our proposed approach on all three databases.

Citation

Mandal, B., Li, L., Chandrasekhar, V., & Lim, J. H. (2015). Whole space subclass discriminant analysis for face recognition. . https://doi.org/10.1109/icip.2015.7350814

• Conference Name: 2015 IEEE International Conference on Image Processing (ICIP)
• Conference Location: Quebec City, QC, Canada
• Start Date: Sep 27, 2015
• End Date: Sep 30, 2015
• Online Publication Date: Dec 10, 2015
• Publication Date: 2015-09
• Deposit Date: Nov 21, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• DOI: https://doi.org/10.1109/icip.2015.7350814
• Publisher URL: https://ieeexplore.ieee.org/document/7350814