DocumentCode :
2458089
Title :
Multiple Granularity-Based Feature Combination for Face Recognition
Author :
Zeng, Xianhua ; Geng, Xinyu
Author_Institution :
Inst. of Comput. Sci. & Technol., Chongqing Univ. of Posts & Telecommun., Chongqing, China
fYear :
2010
fDate :
17-19 Dec. 2010
Firstpage :
557
Lastpage :
560
Abstract :
In face recognition, coarsening-granularity image blocks mainly reflect some contour features and global information of human face identity. On the other hand, refining-granularity face parts can carry more local features about identifying information, for example: mouth, eyes and brows, etc. This paper proposes the framework of multi-granularity feature combination. Under the framework, a multi-granularity feature combination algorithm (MGFC) for face recognition is given, which uses the two-scale windows and two-stage feature extraction method. Some experimental results on benchmark face database demonstrate that MGFC algorithm is effective and can obtain a higher recognition rate than single granularity feature extraction algorithm.
Keywords :
face recognition; feature extraction; MGFC algorithm; coarsening granularity image block; face database; face recognition; global information; human face identity; multigranularity feature combination; multiple granularity based feature combination; two stage feature extraction method; Classification algorithms; Databases; Face; Face recognition; Feature extraction; Principal component analysis; Training; Feature combination; Locality preserving projections; Multi-granularity; SVM;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational and Information Sciences (ICCIS), 2010 International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-8814-8
Electronic_ISBN :
978-0-7695-4270-6
Type :
conf
DOI :
10.1109/ICCIS.2010.142
Filename :
5709062
Link To Document :
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