Face Recognition Using Neural Network and Eigenvalues With Distinct Block Processing
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© March to April Issue 2011 by IJCTT Journal|
|Volume-1 Issue-1 |
|Year of Publication : 2011|
|Authors :Amit Kumar, Prashant Sharma, Shishir Kumar.|
Amit Kumar, Prashant Sharma, Shishir Kumar. "Face Recognition Using Neural Network and Eigenvalues With Distinct Block Processing "International Journal of Computer Trends and Technology (IJCTT),V1(1):4-16 March to April Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract—Human face recognition has been employed in different commercial and law enforcement applications. It has also been employed for mug shots matching, bank-store security, crowd surveillance, expert identification, witness face reconstruction, electronics mug shots book, and electronic lineup. A face recognition system based on principal component analysis and neural networks has been developed. The system consists of three stages; preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were performed. Principal component analysis is applied to obtained the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set with the help of distinct block processing. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces. Neural network is used to create the face database and recognize and authenticate the face by using these weights. In this paper, a separate network was developed for each person. The input face has been projected onto the eigenface space first and new descriptor is obtained. The new descriptor is used as input to each person’s network, trained earlier. The one with maximum output is selected and reported as the equivalent image.
 Goldstein, A. J., Harmon, L. D., and Lesk, A. B., Identification of human faces", Proc. IEEE 59, pp. 748-760, (1971).
 Haig, N. K., "How faces differ - a new comparative technique", Perception 14, pp. 601-615, (1985).
 Kirby, M., and Sirovich, L., "Application of the Karhunen-Loeve procedure for the characterization of human faces", IEEE PAMI, Vol. 12, pp. 103-108, (1990).
 Terzopoulos, D., and Waters, K., "Analysis of facial images using physical and anatomical models", Proc. 3rd Int. Conf. on Computer Vision, pp. 727-732,  F.Galton, “Personal identification and description 1,1 Nature, pp.173-177,21
 Manjunath, B. S., Chellappa, R., and Malsburg, C., "A feature based approach to face recognition", Trans. of IEEE, pp. 373-378, (1992).
 Harmon, L. D., and Hunt, W. F., "Automatic recognition of human face profiles", Computer Graphics and Image Processing, Vol. 6, pp. 135-156, (1977).
 Harmon, L. D., Khan, M. K., Lasch, R., and Ramig, P. F., "Machine identification of human faces", Pattern Recognition, Vol. 13(2), pp. 97- 110,(1981).
 Kaufman, G. J., and Breeding, K. J, "The automatic recognition of human faces from profile silhouettes", IEEE Trans. Syst. Man Cybern., Vol. 6, pp. 113-120, (1976).
 Wu, C. J., and Huang, J. S., "Human face profile recognition by computer", Pattern Recognition, Vol. 23(3/4), pp. 255-259, (1990).
 Kerin, M. A., and Stonham, T. J., "Face recognition using a digital neural network with self-organizing capabilities", Proc. 10th Int. Conf. On Pattern Recognition, pp.738-741, (1990).
 Nakamura, O., Mathur, S., and Minami, T., "Identification of human faces based on isodensity maps", Pattern Recognition, Vol. 24(3), pp. 263-272, (1991).
 Turk, M., and Pentland, A., "Eigenfaces for recognition", Journal of Cognitive Neuroscience, Vol. 3, pp. 71-86, (1991).
 Yuille, A. L., Cohen, D. S., and Hallinan, P. W., "Feature extraction from faces using deformable templates", Proc. of CVPR, (1989). 12
 P.Philips, “The FERET database and evaluation procedure for face recognition algorithms, “ Image and Vision Computing, vol.16, no.5, pp.295-306, 1998
 Carey, S., and Diamond, R., "From piecemeal to configurational representation of faces", Science 195, pp. 312-313, (1977).
 Bledsoe, W. W., "The model method in facial recognition", Panoramic Research Inc. Palo Alto, CA, Rep. PRI:15, (August 1966).
 Bledsoe, W. W., "Man-machine facial recognition", Panoramic Research Inc. Palo Alto, CA, Rep. PRI:22, (August 1966).
 Yuille, A. L., Cohen, D. S., and Hallinan, P. W., "Feature extraction from faces using deformable templates", Proc. of CVPR, (1989).
 Fleming, M., and Cottrell, G., "Categorization of faces using unsupervised feature extraction", Proc. of IJCNN, Vol. 90(2), (1990).
 Kanade, T., "Picture processing system by computer complex and recognition of human faces", Dept. of Information Science, Kyoto University, (1973).
 Ching, C. W., and Huang, L. C., "Human face recognition from a single front view", Int. J. of Pattern Recognition and Artificial Intelligence, Vol. 6(4), pp. 570-593, (1992).
 E. DEDE, “Face Recognition Using Geometric Features and Template Matching By Dimension Reduction”, MSc Thesis, METU, September 2003
 D. Pissarenko (2003). Eigenface-based facial recognition.
 P. Belhumeur, J. Hespanha, and D. Kriegman (july 1997). "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection". IEEE Transactions on pattern analysis and machine intelligence 19 (7).
 L. Sirovich and M. Kirby (1987). "Low-dimensional procedure for the characterization of human faces". Journal of the Optical Society of America A 4: 519–524.
 Burt, P., "Smart sensing within a Pyramid Vision Machine", Proc. of IEEE, Vol. 76(8), pp. 139-153, (1988).
 Characterization of human faces". IEEE Transactions on Pattern analysis and Machine Intelligence 12 (1): 103–108.
 Rein-Lien Hsu, “Face Detection and Modeling for Recognition,” PhD thesis, Department of Computer Science & Engineering, Michigan State University, USA, 2002.
 Henry A.Rowley ,??Neural Network-based face detection?? PhD thesis, Carnegie Mellon University, Pittsburgh, USA, May 1999.
 Kohonen, T., "Self-organization and associative memory", Berlin: Springer- Verlag, (1989).
 Kohonen, T., and Lehtio, P., "Storage and processing of information in distributed associative memory systems", (1981).
 Howard Demuth, Mark Beale, “Neural Network Toolbox User?s Guide For Use with MATLAB”, by The MathWorks, Inc.1998.
 John Daugman, „?Face and Gesture Recognition: Overview?? IEEE PAMI, vol.19, no.7, July 1997.
 M.-H. Yang. N. Ahuja, and D. Kriegman, “Face recognition using Kernel Eigenfaces.” Advances in NIPS,Vol. 14, 2002.
 Q. Liu and S. Ma. “Face Recognition Using Kernel Based Fisher Discriminant Analysis.” IEEE Conf. on Automatic Face and Gesture Recognition, 2002.
 M.-H. Yang. “Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods” IEEE Conf. on Automatic Face and Gesture Recognition, 2002.
 S. Gutta, V. Philomin and M. Trajkovic. “An Investigation into the use of Partial-Faces for Face Recognition” IEEE Conf. on Automatic Face and Gesture Recognition, 2002.
 B. Scholkopf, “Statistical Learning Kernel Methods”. NIPS`00, 2000.  R. Zhang and A. I. Rudnicky. “A large Scale Clustering Scheme for Kernel K-Means” Proc. of ICPR,  Fischler, M. A., and Elschlager, R. A., "The representation and matching of pictorial structures", IEEE Trans. on Computers, c-22.1, (1973).  P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, “The FERET database and evaluation procedure for face recognition algorithms,” Image and Vision Computing J, Vol. 16, No. 5. pp 295-306, 1998.  C..M.Bishop, Neural Networks for Pattern Recognition, Clarendon Press, 1995.  W. Zhao, R. Chellappa, and A. Rosenfeld, “Face recognition: a literature survey”. ACM Computing Surveys, Vol. 35:pp. 399–458, December 2003.  V. Bruce, P.J.B. Hancock and A.M. Burton, “Comparisons between human and computer recognition of faces”, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, Vol., Iss., 14-16 Pages:408-413, Apr 1998
Keywords— Eigenface, eigenvector, eigenvalue, Neural network, distinct block processing, face recognition.