Dimensionality Reduction using SOM based Technique for Face Recognition

Unsupervised or Self-Organized learning algorithms have become very popular for discovery of significant patterns or features in the input data. The three prominent algorithms namely Principal Component Analysis (PCA), Self Organizing Maps (SOM), and Independent Component Analysis (ICA) have widely and successfully been used for face recognition. In this paper a SOM based technique for dimensionality reduction has been proposed. This technique has also been successfully used for face recognition. A comparative study of PCA, SOM and ICA along with the proposed technique for face recognition has also been given. Simulation results indicate that SOM is better than the other techniques for the given face database and the classifier used. The results also show that the performance of the system decreases as the number of classes increase.

[1]  Marian Stewart Bartlett,et al.  Independent component representations for face recognition , 1998, Electronic Imaging.

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

[3]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[4]  Ah Chung Tsoi,et al.  Convolutional neural networks for face recognition , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[7]  D. Kumar,et al.  Face Recognition using Self-Organizing Map and Principal Component Analysis , 2005, 2005 International Conference on Neural Networks and Brain.

[8]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[10]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Victor-Emil Neagoe,et al.  Concurrent self-organizing maps for pattern classification , 2002, Proceedings First IEEE International Conference on Cognitive Informatics.

[12]  Marian Stewart Bartlett,et al.  Independent components of face images : A representation for face recognition , 1997 .

[13]  Young-Seuk Park,et al.  Self-Organizing Map , 2008 .

[14]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[15]  M. Bartlett,et al.  Face image analysis by unsupervised learning and redundancy reduction , 1998 .

[16]  Zhi-Hua Zhou,et al.  Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble , 2005, IEEE Transactions on Neural Networks.

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

[18]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.