An Experimental Comparison of Unsupervised Learning Techniques for Face Recognition
Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
[1] D.L.Swets and J.J.Weng, "Using Discriminant Eigenfeatures for Image
Retrieval," IEEE Trans. on Pattern Analysis and Machine Intelligence,
Vol.18, No.8, pp.831-836, 1996.
[2] P.N.Belhumeur, J.P.Hespanha, D.J.Kriegman, "Eigenfaces Vs
Fisherfaces: Recognition using Class Specific Linear Projection," IEEE
Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.7,
pp.711-720, 1997.
[3] M.Kirby and L.Sirovich, "Application of the Karhunen-Loeve
Procedure for the Characterization of Human Faces," IEEE Trans. on
Pattern Analysis and Machine Intelligence, Vol.12, No.1, pp.103-108,
1990.
[4] A.M.Martinez and A.C.Kak, "PCA versus LDA," IEEE Trans. on
Pattern Analysis and Machine Intelligence, Vol.23, No.2, pp.228-233,
2001.
[5] M.Turk and A.Pentland, "Eigenfaces for Recognition," Journal of
Cognitive Neuroscience, Vol.3, No.1, pp.71-86, 1991.
[6] R. Chellapa, C.L.Wilson, S.Sirobey, "Human and Machine
Recognition of Faces: A Survey," Proc. IEEE, Vol.83, No.5, pp. 705-
740, 1995.
[7] Dinesh Kumar, C.S.Rai and Shakti Kumar, "Self Organizing maps for
Face Recognition: Local vs Global Processing," Proceedings of
International Conference on Systemics, Cybernetics and Informatics,
ICSCI-2007, Vol.1, pp 758-761, Jan. 2007, Hyderabad, India.
[8] W.Zhao, R. Chellapa, A. Rosenfeld, P.J Phillips, "Face Recognition: A
Literature Survey," ACM Computing Surveys, Vol.35, No.4, pp. 399-
458, 2003.
[9] Dinesh Kumar, C.S.Rai and Shakti Kumar, "Face recognition using
Self Organizing Map and Principal Component Analysis," Proceedings
of IEEE International Conference on Neural Networks and Brain,
ICNNB-2005, Vol. 3, pp 1469-1473, Oct. 2005, Beijing, China.
[10] C.Liu and H.Wechsler, "Evolutionary Pursuit and its Applications to
Face Recognition," IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol.22, No.6, pp.570-582, 2000.
[11] M.S.Bartlett and T.J.Sejnowski, "Independent Components of Face
Images: A Representation for Face Recognition," Proc. of the 4th
Annual Joint Symposium on Neural Computation, Pasadena, 1997.
[12] B.Moghaddam, "Principal Manifolds and Bayesian Subspaces for
Visual Recognition," International Conference on Computer Vision,
Greece, pp.1131-1136, 1999.
[13] B.Moghaddam and A.Pentland, "Probabilistic Visual Learning for
Object Detection," Proc. International Conference on Computer Vision,
pp. 786-793, 1995.
[14] M.S.Bartlett, H.M.Lades, T.J.Sejnowski, "Independent Component
Representations for Face Recognition," Proc. of SPIE Symposium on
Electronic Imaging: Science and Technology; Conference on Human
Vision and Electronic Imaging III, California, 1998.
[15] M.S.Bartlett, "Face Image Analysis by Unsupervised Learning and
Redundancy Reduction," Ph.D. Dissertation, University of California,
San Diego, 1998.
[16] B.Moghaddam and A.Pentland, "Probabilistic Matching for Face
Recognition," IEEE Southwest Symposium on Image Analysis and
Interpretation, pp. 186-191,1998.
[17] B.Moghaddam, T.Jebara, A.Pentland, "Efficient MAP/ML Similarity
Matching for Visual Recognition," Proc. of Fourteenth International
Conference on Pattern recognition, Vol. 1, pp.876-881, 1998.
[18] X.Tan, S.Chen, z.H.Zhou, F.Zhang, "Recognizing Partially Occluded,
Expression Variant Faces from Single Training Image per Person with
SOM and Soft k-NN Ensemble,"IEEE Trans. on Neural Networks,
Vol.16, No. 4, pp. 875-886, 2005.
[19] V.E.Neagoe, A.D.Ropot, "Concurrent Self Organizing Maps for
Pattern Classification," Proc. of First International Conference on
Cognitive Informatics, ICCI-02, pp. 304-312, 2002.
[20] S.Lawrence,C.L.Giles,A.C.Tsoi, "Convolutional Neural Networks for
Face Recognition , " Proc. of IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, pp..217-222, 1996.
[21] M.S.Bartlett, J.R.Movellan and T.J.Sejnowski, "Face Recognition by
Independent Component Analysis," IEEE Trans. on Neural Networks,
Vol.13, No.6, pp.1450-1464, 2002.
[22] http://www.cam-orl.co.uk/facedatabase.html.
[23] Simon Haykin, "Neural Networks - A Comprehensive Foundation," 2nd
Edition, Pearson Education, 1999.
[24] A.J.Bell and T.J.Sejnowski, "An Information-maximization approach
to blind separation and blind deconvolution," Neural computation,
Vol.7, No.6, pp. 1129-1159, 1995.
[25] A.Hyvärinen, "Survey on Independent Component Analysis," Neural
Computing Surveys, Vol.2, pp.94-128, 1999.
[26] A.Hyvärinen and E.Oja, "Independent Component Analysis:
Algorithms and Applications," Neural Networks, Vol.3, No. 4-5,
pp.411- 430, 2000.
[27] S.Z.Li and J.Lu, "Face Recognition using Nearest Feature Line
Method," IEEE Trans. on Neural Networks, Vol.10, No.2, pp.439-443,
1999.
[28] P.J.Phillips, "Support Vector Machines Applied to Face Recognition,"
Technical Report, NISTIR 6241.
[29] X.He, S.Yan, Y.Hu, P.Niyogi, H.J.Zhang, "Face Recognition using
Laplacianfaces," IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol.27, No.3, pp.328-340, 2005.
[30] T.Kohonen, "Self Organization and Associative Memory," 2nd Edition,
Berlin, Germany: springer-Verlag, 1988.
[31] T.Kohonen, "Self Organizing Map," 2nd Edition Berlin, Germany:
Springer-verlag, 1997.
[1] D.L.Swets and J.J.Weng, "Using Discriminant Eigenfeatures for Image
Retrieval," IEEE Trans. on Pattern Analysis and Machine Intelligence,
Vol.18, No.8, pp.831-836, 1996.
[2] P.N.Belhumeur, J.P.Hespanha, D.J.Kriegman, "Eigenfaces Vs
Fisherfaces: Recognition using Class Specific Linear Projection," IEEE
Trans. on Pattern Analysis and Machine Intelligence, Vol.19, No.7,
pp.711-720, 1997.
[3] M.Kirby and L.Sirovich, "Application of the Karhunen-Loeve
Procedure for the Characterization of Human Faces," IEEE Trans. on
Pattern Analysis and Machine Intelligence, Vol.12, No.1, pp.103-108,
1990.
[4] A.M.Martinez and A.C.Kak, "PCA versus LDA," IEEE Trans. on
Pattern Analysis and Machine Intelligence, Vol.23, No.2, pp.228-233,
2001.
[5] M.Turk and A.Pentland, "Eigenfaces for Recognition," Journal of
Cognitive Neuroscience, Vol.3, No.1, pp.71-86, 1991.
[6] R. Chellapa, C.L.Wilson, S.Sirobey, "Human and Machine
Recognition of Faces: A Survey," Proc. IEEE, Vol.83, No.5, pp. 705-
740, 1995.
[7] Dinesh Kumar, C.S.Rai and Shakti Kumar, "Self Organizing maps for
Face Recognition: Local vs Global Processing," Proceedings of
International Conference on Systemics, Cybernetics and Informatics,
ICSCI-2007, Vol.1, pp 758-761, Jan. 2007, Hyderabad, India.
[8] W.Zhao, R. Chellapa, A. Rosenfeld, P.J Phillips, "Face Recognition: A
Literature Survey," ACM Computing Surveys, Vol.35, No.4, pp. 399-
458, 2003.
[9] Dinesh Kumar, C.S.Rai and Shakti Kumar, "Face recognition using
Self Organizing Map and Principal Component Analysis," Proceedings
of IEEE International Conference on Neural Networks and Brain,
ICNNB-2005, Vol. 3, pp 1469-1473, Oct. 2005, Beijing, China.
[10] C.Liu and H.Wechsler, "Evolutionary Pursuit and its Applications to
Face Recognition," IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol.22, No.6, pp.570-582, 2000.
[11] M.S.Bartlett and T.J.Sejnowski, "Independent Components of Face
Images: A Representation for Face Recognition," Proc. of the 4th
Annual Joint Symposium on Neural Computation, Pasadena, 1997.
[12] B.Moghaddam, "Principal Manifolds and Bayesian Subspaces for
Visual Recognition," International Conference on Computer Vision,
Greece, pp.1131-1136, 1999.
[13] B.Moghaddam and A.Pentland, "Probabilistic Visual Learning for
Object Detection," Proc. International Conference on Computer Vision,
pp. 786-793, 1995.
[14] M.S.Bartlett, H.M.Lades, T.J.Sejnowski, "Independent Component
Representations for Face Recognition," Proc. of SPIE Symposium on
Electronic Imaging: Science and Technology; Conference on Human
Vision and Electronic Imaging III, California, 1998.
[15] M.S.Bartlett, "Face Image Analysis by Unsupervised Learning and
Redundancy Reduction," Ph.D. Dissertation, University of California,
San Diego, 1998.
[16] B.Moghaddam and A.Pentland, "Probabilistic Matching for Face
Recognition," IEEE Southwest Symposium on Image Analysis and
Interpretation, pp. 186-191,1998.
[17] B.Moghaddam, T.Jebara, A.Pentland, "Efficient MAP/ML Similarity
Matching for Visual Recognition," Proc. of Fourteenth International
Conference on Pattern recognition, Vol. 1, pp.876-881, 1998.
[18] X.Tan, S.Chen, z.H.Zhou, F.Zhang, "Recognizing Partially Occluded,
Expression Variant Faces from Single Training Image per Person with
SOM and Soft k-NN Ensemble,"IEEE Trans. on Neural Networks,
Vol.16, No. 4, pp. 875-886, 2005.
[19] V.E.Neagoe, A.D.Ropot, "Concurrent Self Organizing Maps for
Pattern Classification," Proc. of First International Conference on
Cognitive Informatics, ICCI-02, pp. 304-312, 2002.
[20] S.Lawrence,C.L.Giles,A.C.Tsoi, "Convolutional Neural Networks for
Face Recognition , " Proc. of IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, pp..217-222, 1996.
[21] M.S.Bartlett, J.R.Movellan and T.J.Sejnowski, "Face Recognition by
Independent Component Analysis," IEEE Trans. on Neural Networks,
Vol.13, No.6, pp.1450-1464, 2002.
[22] http://www.cam-orl.co.uk/facedatabase.html.
[23] Simon Haykin, "Neural Networks - A Comprehensive Foundation," 2nd
Edition, Pearson Education, 1999.
[24] A.J.Bell and T.J.Sejnowski, "An Information-maximization approach
to blind separation and blind deconvolution," Neural computation,
Vol.7, No.6, pp. 1129-1159, 1995.
[25] A.Hyvärinen, "Survey on Independent Component Analysis," Neural
Computing Surveys, Vol.2, pp.94-128, 1999.
[26] A.Hyvärinen and E.Oja, "Independent Component Analysis:
Algorithms and Applications," Neural Networks, Vol.3, No. 4-5,
pp.411- 430, 2000.
[27] S.Z.Li and J.Lu, "Face Recognition using Nearest Feature Line
Method," IEEE Trans. on Neural Networks, Vol.10, No.2, pp.439-443,
1999.
[28] P.J.Phillips, "Support Vector Machines Applied to Face Recognition,"
Technical Report, NISTIR 6241.
[29] X.He, S.Yan, Y.Hu, P.Niyogi, H.J.Zhang, "Face Recognition using
Laplacianfaces," IEEE Trans. on Pattern Analysis and Machine
Intelligence, Vol.27, No.3, pp.328-340, 2005.
[30] T.Kohonen, "Self Organization and Associative Memory," 2nd Edition,
Berlin, Germany: springer-Verlag, 1988.
[31] T.Kohonen, "Self Organizing Map," 2nd Edition Berlin, Germany:
Springer-verlag, 1997.
@article{"International Journal of Information, Control and Computer Sciences:49437", author = "Dinesh Kumar and C.S. Rai and Shakti Kumar", title = "An Experimental Comparison of Unsupervised Learning Techniques for Face Recognition ", abstract = "Face Recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal Component Analysis (PCA), Self Organizing Maps (SOM) and Independent Component Analysis (ICA) are the three techniques among many others as proposed by different researchers for Face Recognition, known as the unsupervised techniques. This paper proposes integration of the two techniques, SOM and PCA, for dimensionality reduction and feature selection. Simulation results show that, though, the individual techniques SOM and PCA itself give excellent performance but the combination of these two can also be utilized for face recognition. Experimental results also indicate that for the given face database and the classifier used, SOM performs better as compared to other unsupervised learning techniques. A comparison of two proposed methodologies of SOM, Local and Global processing, shows the superiority of the later but at the cost of more computational time.
", keywords = "Face Recognition, Principal Component Analysis,
Self Organizing Maps, Independent Component Analysis", volume = "1", number = "4", pages = "816-9", }
