DocumentCode :
634134
Title :
Face recognition using global and local Gabor features
Author :
Nazari, Sara ; Moin, Mohammad-Shahram
Author_Institution :
Fac. of Electr. & Comput. Eng., Islamic Azad Univ., Qazvin, Iran
fYear :
2013
fDate :
14-16 May 2013
Firstpage :
1
Lastpage :
4
Abstract :
Facial feature selection is one of the most important challenges in a face recognition system. In a face image, only a part of a face image is changed due to illumination, pose, and other source of changes. In this paper, a novel face recognition approach is proposed based on fusing global and local features. To extract global and local features, we employed Gabor wavelet filter to apply on whole image and non-overlapping sub-images with equal size. To reduce the dimension of new fused feature vector, Principal Component Analysis (PCA) technique is employed. In our experiments, we used KNN and multi-class SVM classifiers and ORL database to obtain face recognition rate. The results show that the new face recognition algorithm outperforms the conventional methods such as global Gabor face recognition, and G-2DFLD feature fusion face recognition in term of recognition rate.
Keywords :
Gabor filters; face recognition; feature extraction; image fusion; pattern classification; principal component analysis; support vector machines; wavelet transforms; G-2DFLD feature fusion face recognition; Gabor wavelet filter; KNN; ORL database; PCA; facial feature selection; global Gabor face recognition; global features; illumination; local Gabor features; multiclass SVM classifiers; principal component analysis; Classification algorithms; Databases; Face; Face recognition; Feature extraction; Principal component analysis; Support vector machine classification; Facial feature extraction; Gabor wavelet; Global feature; Local feature; PCA;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering (ICEE), 2013 21st Iranian Conference on
Conference_Location :
Mashhad
Type :
conf
DOI :
10.1109/IranianCEE.2013.6599704
Filename :
6599704
Link To Document :
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