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Face Recognition by Embedding of DT-CWT Coefficient Using SOM and Ensemble Based Classifier


Affiliations
1 Department of Electronics and Communication Engineering, B.S.A. College of Engineering and Technology, India
2 Department of Electrical Engineering, GLA University, India
     

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In real world, applications designing of a robust face recognition system have always been a big challenge. This paper presents an approach to face recognition using embedding of dual tree complex wavelet transform, Self-organizing map and ensemble of weak classifier. The DT-CWT is applied on the images to obtain dynamic and multi scale informational characterization of the face images. Thus, a multidimensional feature vector is formed by combining DT-CWT coefficients. Self Organizing Map (SOM) is further used in embedding the feature vector which also results in reduction of feature vector. Finally, ensembles of k-Nearest Neighbor (k-NN) weak classifiers are used for classification of recognition system. The proposed approach is tested on image of ORL database. The experiment shows an impressive recognition result that culminated during the testing.

Keywords

Face Recognition, Wavelet Transform, Self-Organizing Map (SOM).
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  • Face Recognition by Embedding of DT-CWT Coefficient Using SOM and Ensemble Based Classifier

Abstract Views: 170  |  PDF Views: 3

Authors

Gauri Agrawal
Department of Electronics and Communication Engineering, B.S.A. College of Engineering and Technology, India
Sanjay Kumar Maurya
Department of Electrical Engineering, GLA University, India

Abstract


In real world, applications designing of a robust face recognition system have always been a big challenge. This paper presents an approach to face recognition using embedding of dual tree complex wavelet transform, Self-organizing map and ensemble of weak classifier. The DT-CWT is applied on the images to obtain dynamic and multi scale informational characterization of the face images. Thus, a multidimensional feature vector is formed by combining DT-CWT coefficients. Self Organizing Map (SOM) is further used in embedding the feature vector which also results in reduction of feature vector. Finally, ensembles of k-Nearest Neighbor (k-NN) weak classifiers are used for classification of recognition system. The proposed approach is tested on image of ORL database. The experiment shows an impressive recognition result that culminated during the testing.

Keywords


Face Recognition, Wavelet Transform, Self-Organizing Map (SOM).