DocumentCode :
3562598
Title :
Face recognition using adaptive filter wavelet transform based feature extraction
Author :
Sanket, Nitin J. ; Vyshak, A.V. ; Manikantan, K. ; Ramachandran, S.
Author_Institution :
Dept. of Electron. & Commun. Eng., M.S.R. Inst. of Tech, Bangalore, India
fYear :
2014
Firstpage :
1
Lastpage :
9
Abstract :
Face Recognition (FR) under varying pose, illumination and occlusion conditions is challenging. In this paper, a novel algorithm called Mirrored Fusion is proposed to normalize the effects of pose variations in facial images. A unique feature extraction technique called Adaptive Filter Wavelet Transform (AFWT) is proposed, which is a combination of Stationary Wavelet Transform (SWT) along with Wiener filtering and scaling, Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). AFWT results in low contrast images with prominent features which are desirable for enhanced recognition rate of the FR system. Experimental results obtained by applying the proposed algorithm on FERET, Pointing Head Pose, CMU PIE, Extended YaleB and Georgia Tech face databases show that the proposed system outperforms other FR systems.
Keywords :
Wiener filters; adaptive filters; discrete cosine transforms; discrete wavelet transforms; face recognition; feature extraction; image fusion; pose estimation; visual databases; AFWT; CMU PIE; DCT; DWT; FERET; Georgia Tech face databases; SWT; Wiener filtering; adaptive filter wavelet transform; discrete cosine transform; discrete wavelet transform; extended YaleB; face recognition; facial images; feature extraction; illumination; mirrored fusion; occlusion; pointing head pose; stationary wavelet transform; varying pose; Adaptive filters; Discrete wavelet transforms; Feature extraction; Hafnium; Skin; Discrete Cosine Transform; Discrete Wavelet Transform; Face Recognition; Feature Extraction; Feature Selection; Particle Swarm Optimization; Stationary Wavelet Transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Science Engineering and Management Research (ICSEMR), 2014 International Conference on
Print_ISBN :
978-1-4799-7614-0
Type :
conf
DOI :
10.1109/ICSEMR.2014.7043555
Filename :
7043555
Link To Document :
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