Title :
A sparse representation method with maximum probability of partial ranking for face recognition
Author :
Yi-Haur Shiau ; Chaur-Chin Chen
Author_Institution :
Dept. of Comput. Sci., Nat. Tsing Hua Univ., Hsinchu, Taiwan
fDate :
Sept. 30 2012-Oct. 3 2012
Abstract :
Face recognition is a popular topic in computer vision applications. Compressive sensing is a novel sampling technique for finding sparse solutions to underdetermined linear systems. Recently, a sparse representation-based classification (SRC) method based on compressive sensing is presented. It has been shown to be robust for face recognition. In this paper, we proposed a maximum probability of partial ranking method based on the framework of SRC, called SRC-MP. It computes the maximum probability from the largest λ weighting coefficients for the individuals, respectively. Experiments are implemented on Extended Yale B and ORL face databases using eigenfaces, fisherfaces, 2DPCA and 2DLDA for feature extraction. Furthermore, we compare our proposed method with classical projection-based methods such as principal component analysis (PCA), linear discriminant analysis (LDA), 2DPCA and 2DLDA. The experimental results demonstrate our proposed method is able to achieve higher recognition rate than other methods.
Keywords :
compressed sensing; computer vision; face recognition; feature extraction; image representation; principal component analysis; probability; sampling methods; 2DLDA; 2DPCA; ORL face database; SRC method; SRC-MP; classical projection-based methods; compressive sensing; computer vision applications; eigenfaces; extended Yale B face database; face recognition; feature extraction; fisherfaces; largest λ weighting coefficients; linear discriminant analysis; maximum probability; novel sampling technique; partial ranking; principal component analysis; sparse representation-based classification method; underdetermined linear systems; Compressed sensing; Databases; Face; Face recognition; Principal component analysis; Testing; Training; Compressive sensing; face recognition; linear discriminant analysis; principal component analysis; sparse representation classification;
Conference_Titel :
Image Processing (ICIP), 2012 19th IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4673-2534-9
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2012.6467142