Survey of Face Recognition Techniques

Face recognition is a kind of automated biometric identification technique that recognizes an individual based on their facial features as essential elements of distinction. The research on face recognition has been actively going on in the recent years because face recognition spans numerous fields and disciplines such as access control, surveillance and security, credit-card verification, criminal identification and digital library. In this paper we discuss past research on biometric face feature extraction and recognition of static images. We will present implementation outline of these methods along with their comparative measures.

[1]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[2]  Mislav Grgic,et al.  Generalization Abilities of Appearance-Based Subspace Face Recognition Algorithms , 2005 .

[3]  W. Marsden.  I and J , 2012 .

[4]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[5]  Konstantinos N. Plataniotis,et al.  Face recognition using LDA-based algorithms , 2003, IEEE Trans. Neural Networks.

[6]  Timothy F. Cootes,et al.  An Automatic Face Identification System Using Flexible Appearance Models , 1994, BMVC.

[7]  Sanghoon Kim,et al.  Computational Analysis of PCA-based Face Recognition Algorithms , 2003 .

[8]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

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

[10]  LeeTai Sing.  Image Representation Using 2D Gabor Wavelets , 1996 .

[11]  Zezhi Chen,et al.  Face Recognition: A Comparison of Appearance-Based Approaches , 2003, DICTA.

[12]  Rama Chellappa,et al.  A feature based approach to face recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[13]  K. Etemad,et al.  Discriminant analysis for recognition of human face images , 1997 .

[14]  Ferdinando Samaria,et al.  Face Segmentation For Identification Using Hidden Markov Models , 1993, BMVC.

[15]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[16]  Matthew Turk,et al.  A Random Walk through Eigenspace , 2001 .

[17]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[18]  Stefan Fischer,et al.  Face authentication with Gabor information on deformable graphs , 1999, IEEE Trans. Image Process..

[19]  M. Grgic,et al.  Appearance-based statistical methods for face recognition , 2005, 47th International Symposium ELMAR, 2005..

[20]  Kevin W. Bowyer,et al.  Empirical evaluation techniques in computer vision , 1998 .

[21]  Sun Da.  Feature-Based Face Recognition by Elastic Graph Matching , 2002 .

[22]  Rama Chellappa,et al.  Face recognition using discriminant eigenvectors , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

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

[24]  Steve J. Young,et al.  HMM-based architecture for face identification , 1994, Image Vis. Comput..

[25]  Vytautas Perlibakas.  Face Recognition Using Principal Component Analysis and Wavelet Packet Decomposition , 2004, Informatica.

[26]  Marco Furini,et al.  International Journal of Computer and Applications , 2010 .

[27]  H Moon,et al.  Computational and Performance Aspects of PCA-Based Face-Recognition Algorithms , 2001, Perception.