Title :
Human Face Recognition Using Generalized Kernel Fisher Discriminant and Wavelet Transform
Author :
Cao, Wen-Gang ; Jiang, Kang ; Yu, Zhen-Hua ; Sun, Bing-Yu
Author_Institution :
Sch. of Mech. & Automotive Eng., Hefei Univ. of Technol., Hefei
Abstract :
In this paper the generalized kernel Fisher discriminant (GKFD) method is used to do pattern feature extraction for human face image. First, we extend the KFD originally used in pattern classification problems to the generalized KFD (GKFD), which will be used in feature extraction problems. Compared to several commonly used feature extraction methods, the GKFD can not only reduce the dimension of input pattern, but also provide the useful information for pattern classification. Further, this GKFD also performs well for linearly nonseparable pattern classification problems for it possesses a nonlinear transformation capability. To reduce the computation complexity, the original face images are pre-processed by wavelet transform. Finally, the experimental results on human face recognition problems demonstrate the effectiveness and efficiency of our approach.
Keywords :
face recognition; feature extraction; image classification; wavelet transforms; computation complexity; face image; generalized kernel Fisher discriminant; human face recognition; pattern classification; pattern feature extraction; wavelet transform; Face recognition; Feature extraction; Humans; Kernel; Nearest neighbor searches; Pattern classification; Pattern recognition; Principal component analysis; Scattering; Wavelet transforms; face recognition; feature extraction; kerenl fisher discriminant; nearest neighbor;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305930