Combination of Wavelet and PCA for face recognition

This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method on the Yale and ORL face databases. Moreover, the accuracy of the proposed method is improved.

[1]  Xiaobo Li,et al.  Towards a system for automatic facial feature detection , 1993, Pattern Recognit..

[2]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[3]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[4]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[5]  Hong Yan,et al.  Locating and extracting the eye in human face images , 1996, Pattern Recognit..

[6]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[7]  Alice J. O'Toole,et al.  Connectionist models of face processing: A survey , 1994, Pattern Recognit..

[8]  E. Micheli-Tzanakou,et al.  Comparison of Neural Network Algorithms for Face Recognition , 1995, Simul..

[9]  Alan L. Yuille,et al.  Feature extraction from faces using deformable templates , 2004, International Journal of Computer Vision.

[10]  Philip D. Wasserman,et al.  Neural computing - theory and practice , 1989 .

[11]  Kazuo Kyuma,et al.  Face Recognition System Using Local Autocorrelations and Multiscale Integration , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  M. Victor Wickerhauser,et al.  Large-Rank Approximate Principal Component Analysis with Wavelets for Signal Feature Discrimination and the Inversion of Complicated Maps , 1994, J. Chem. Inf. Comput. Sci..

[13]  L. D. Harmon.  The recognition of faces. , 1973, Scientific American.

[14]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Y Sheng,et al.  Wavelet transform as a bank of the matched filters. , 1992, Applied optics.

[16]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  M. Victor Wickerhauser,et al.  Wavelets: Algorithms and Applications (Yves Meyer) , 1994, SIAM Rev..

[18]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[19]  Upkar Varshney,et al.  Architecture and Performance of MLAN: A Multimedia Local ATM Network , 1995, Simul..

[20]  Jen-Tzung Chien,et al.  Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Alice J. O'Toole,et al.  Low-dimensional representation of faces in higher dimensions of the face space , 1993 .

[22]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[23]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[25]  A. O'Toole,et al.  Principal Component and Neural Network Analyses of Face Images: Explorations into the Nature of Information Available for Classifying Faces by Sex , 1996 .

[26]  Stan Z. Li,et al.  Face recognition using the nearest feature line method , 1999, IEEE Trans. Neural Networks.