A New Biologically Inspired Pattern Recognition Spproach for Face Recognition
This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081031Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1264
 D. J. K. Ming-Hsuan Yang and N. Ahuja, "Detecting faces in images: A survey," IEEE Transactions on Pattern Analysis and Machine Intelli¬gence, pp. 34-58, 2002.
 J. Daugman, "Face detection: A survey," Computer Vision and Image Understanding, vol. 83, pp. 236-274, 2001.
 M. A. Grudin, "On internal representation of face recognition systems," Pattern Recognition, vol. 33, pp. 1161-1177, 2000.
 P. P. W. Zhao, R. Chellappa and A. Rosenfeld, "Face recognition: A literature survey," ACM Computing Surveys, vol. 35, pp. 399-458, 2003.
 S. Z. Li and A. K. Jain, Handbook of Face Recognition. Springer, 2004.
 J. Haddadnia and M. Ahmadi, "N-feature neural network human face recognition," Image and Vision Computing, vol. 22, p. 1071, 2004.
 M. J. Haddadnia and K. Faez, "An efficient method for recognition of human face using high order pseudo zernike moment invariant," in 5th IEEE International Conference on Automatic Face and Gesture Recognition, Washington, DC,USA, 2000, pp. 315-32.
 J. Wang and T. Tan, "A new face detection method based on shape information," Pattern Recognition Letter, vol. 21, pp. 463-471, 2000.
 M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of Cognitive Neurosicence, vol. 3, pp. 71-86, 1991.
 B. C. L. Shaokang Chen and T. Shan, "Robust adapted principal com¬ponent analysis for face recognition," International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), vol. 23, pp. 491-520, 2009.
 M. M.S. Bartlett and T. Sejnowski, "Face recognition by independent component analysis," IEEE Trans. on Neural Networks, vol. 13, pp. 1450-1464, 2002.
 Etemad and R. Chellappa, "Discriminant analysis for recognition of human face images," Journal of the Optical Society of America, vol. 14, pp. 1724-1733, 1997.
 X. L. Dong Xu, Dacheng Tao and S. Yan, "Face recognition - a generalized marginal fisher analysis approach," International Journal of Image and Graphics (IJIG), vol. 7, pp. 583-591, 2007.
 W.-S. Chen, "Wavelet-face based subspace lda method to solve small sample size problem in face recognition," International Journal of Wavelets,Multiresolution and Information Processing (IJWMIP), vol. 7, pp. 199-214, 2009.
 C. Liu and H. Wechsler, "Evolutionary pursuit and its application to face recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, pp. 570-582, 2000.
 N. L. Wiskott, J.-M. Fellous and C. Malsburg, Face Recognition by Elastic Bunch Graph Matching Intelligent Biometric Techniques in Fingerprint and Face Recognition, L. J. et al, Ed. CRC Press, 1999.
 A.Kadyrov and M. Petrou, "The trace transform and its applications," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, pp. 811-828, 2001.
 K. W. T.F. Cootes and C. Taylor, "View-based active appearance models," IEEE International Conference on Automatic Face and Gesture Recognition, vol. 17, pp. 227-232, 2000.
 A. Nefian and M. Hayes, "Maximum likelihood training of the embedded hmm for face detection and recognition," IEEE International Conference on Image Processing, pp. 33-36, 2000.
 S. L. G. Guo and K. Chan, "Face recognition by support vector ma-chines," IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196-201, 2000.
 I. Daubechies, "The wavelet transform, time-frequency localization and signal analysis," IEEE Transactions on Information Theory, vol. 36, pp. 961-1005, 1990.
 S. Mallat, "Multifrequencies channel decompositionsof images and wavelets models," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37, 1989.
 G. Z. Christophe Garcia and G. Tziritas, "A wavelet-based framework for face recognition," ICS - Foundation for Research and Technology-Hellas, vol. fourth, 2000.
 R. C. Gonzales and R. E. Woods, Digital Image Processing. Pearson Edition, 2002.
 J. Tou and R. Gonzalez, Pattern Recognition Principle. Addison Wesley, 1975.
 B. Chanda and D. D. Majumder, Digital Image Processing and Analysis. PHI, 2000.
 S. Haykin, Neural Networks a comprehensive foundation. Pearson Education, 2001.
 T. Kohonen, "An introduction to neural computing," Neural Networks, vol. 1, pp. 3-16, 1988.
 B. Yegnanarayana, Artificial neural networks. PHI, 2005.