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Statistical Models for Face Recognition System With Different Distance Measures
R.Thiyagarajan, S. Arulselvi, G.Sainarayanan
Pages - 647 - 660     |    Revised - 31-01-2011     |    Published - 08-02-2011
Volume - 4   Issue - 6    |    Publication Date - January / February  Table of Contents
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KEYWORDS
Face Recognition, Statistical Models, Distance measure methods, PCA/LDA/ICA
ABSTRACT
Face recognition is one of the challenging applications of image processing. Robust face recognition algorithm should posses the ability to recognize identity despite many variations in pose, lighting and appearance. Principle Component Analysis (PCA) method has a wide application in the field of image processing for dimension reduction of the data. But these algorithms have certain limitations like poor discriminatory power and ability to handle large computational load. This paper proposes a face recognition techniques based on PCA with Gabor wavelets in the preprocessing stage and statistical modeling methods like LDA and ICA for feature extraction. The classification for the proposed system is done using various distance measure methods like Euclidean Distance(ED), Cosine Distance (CD), Mahalanobis Distance (MHD) methods and the recognition rate were compared for different distance measures. The proposed method has been successfully tested on ORL face data base with 400 frontal images corresponding to 40 different subjects which are acquired under variable illumination and facial expressions. It is observed from the results that use of PCA with Gabor filters and features extracted through ICA method gives a recognition rate of about 98% when classified using Mahalanobis distance classifier. This recognition rate stands better than the conventional PCA and PCA + LDA methods employing other and classifier techniques.
CITED BY (3)  
1 Vijayalakshmi, G. V., Raj, A. N. J., & Ashok Varma, S. V. S. K. (2014, October). Optimum selection of features for 2D (color) and 3D (depth) face recognition using modified PCA (2D). In Smart Structures and Systems (ICSSS), 2014 International Conference on (pp. 1-7). IEEE.
2 Kurniawan, D. E. Identifikasi Citra Wajah Menggunakan Gabor-based Kernel Principal Component Analysis.
3 Wu, F., Xiao, Q., & Vo, T. D. (2013). Face image database: a test-bed for evaluation and certification of facial recognition systems. International journal of biometrics, 5(3-4), 211-228.
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Mr. R.Thiyagarajan
Annamalai University - India
thiyagucdm@gmail.com
Mr. S. Arulselvi
- India
Mr. G.Sainarayanan
- India