Title :
Accelerated robust sparse coding for fast face recognition
Author :
Guanglu Liu ; Yan Yanand ; Hanzi Wang
Author_Institution :
Sch. of Inf. Sci. & Technol., Xiamen Univ., Xiamen, China
Abstract :
In this paper, we propose an accelerated robust sparse coding (ARSC) method which is based on the combination of linear-regression based classification and robust sparse coding for fast face recognition. First, linear-regression based classification (LRC) is used to select the candidate face set, which can effectively reduce the search scope. Then, robust sparse coding (RSC) is applied to perform accurate face identification in the selected face set. Extensive experimental results on various face databases demonstrate that the proposed ARSC can greatly reduce the computational complexity while achieving high recognition performance.
Keywords :
computational complexity; face recognition; image classification; image coding; regression analysis; search problems; visual databases; ARSC method; LRC; accelerated robust sparse coding method; candidate face set; computational complexity; face databases; face identification; fast face recognition; linear-regression based classification; search scope; Computational complexity; Databases; Encoding; Face; Face recognition; Lighting; Robustness;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4