FACE RECOGNITION USING DIFFERENT LEVEL OF ALGORITHMS

This paper proposes the automatic face recognition method based on the face representation with five major processing modules- Filters, Face Location, Feature Location, Normalization, and Face Recognition. This precisely reflects the geometric features of the specific subject. We test our proposed algorithm database, and experimental results, show the effectiveness and competitive performance of the proposed method.

[1]  Seong-Whan Lee,et al.  Authenticating corrupted face image based on noise model , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[2]  M. W. Koch,et al.  3D facial recognition: a quantitative analysis , 2004, 38th Annual 2004 International Carnahan Conference on Security Technology, 2004..

[3]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[4]  Daoqiang Zhang,et al.  Enhanced (PC)2 A for face recognition with one training image per person , 2004, Pattern Recognit. Lett..

[5]  Zhi-Hua Zhou,et al.  Face recognition from a single image per person: A survey , 2006, Pattern Recognit..

[6]  J. Woodward,et al.  Biometrics: A Look at Facial Recognition , 2003 .

[7]  Daoqiang Zhang,et al.  A new face recognition method based on SVD perturbation for single example image per person , 2005, Appl. Math. Comput..

[8]  Wen Gao,et al.  Extended Fisherface for face recognition from a single example image per person , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[9]  Jie Wang,et al.  Selecting discriminant eigenfaces for face recognition , 2005, Pattern Recognit. Lett..

[10]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Ping Fu,et al.  Sampled FLDA for face recognition with single training image per person , 2006, Neurocomputing.