Feature selection method based on sparse representation classification for face recognition

Boon, Yinn Xi * and Ch'ng, Sue Inn * (2014) Feature selection method based on sparse representation classification for face recognition. In: International Conference Image Processing, Computers and Industrial Engineering (ICICIE '2014), 15 -16 Jan 2014, Kuala Lumpur. (Submitted)

DCIS_Ching Sue Inn. Feature selection method.pdf

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Compressed sensing is a signal processing technique. The entity signal can be efficiently reconstructed if the sparse representation is determined. The sparse representations of all the test images are determined with respect to the training set by computing the l1-minimization. However, sparse representation which involves high dimensional feature vector is computationally expensive. Thus, discriminative features that could perform accurately for the face recognition system under visual variations, such as illumination, expression and occlusion have to be selected carefully. In this paper, feature selection method in the application of face recognition based on sparse representation classifier (SRC) is proposed. The proposed technique first divides the images of a few subjects into chunks. Then, it selects the feature subsets based on distance based measurement, the residual, and recognition performance, the accuracy. Extensive experiments with visual variations are carried out by using ORL, AR and Yale databases.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Both authors are with the Dept. of Computer Science and Networked System, Faculty of Science and Technology, Sunway University.
Uncontrolled Keywords: Sparse representation; face recognition; compressed sensing; feature selection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Sunway University > School of Engineering and Technology [formerly School of Science and Technology until 2020] > Dept. Computing and Information Systems
Depositing User: Ms. Molly Chuah
Related URLs:
Date Deposited: 01 Apr 2015 02:35
Last Modified: 25 Apr 2019 06:02
URI: http://eprints.sunway.edu.my/id/eprint/255

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