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A Priori Laplacian with Hamming Distance: Advanced Dimension Reduction Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 77 - Number 9 |
| Year of Publication: 2013 |
| Authors: Gaurav Gupta |
10.5120/13426-1112
|
Gaurav Gupta . A Priori Laplacian with Hamming Distance: Advanced Dimension Reduction Technique. International Journal of Computer Applications. 77, 9 ( September 2013), 42-48. DOI=10.5120/13426-1112
Abstract
Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. The rapidly expanding research in face processing is based on the premise that information about a user's identity, state, and intent can be extracted from images and that computers can then react accordingly, e. g. , by knowing person's identity, person may be authenticated to utilize a particular service or not. A first step of any face processing system is registering the locations in images where faces are present. However, face registration for whole database is a challenging task because of variability in scale, location, orientation (up-right, rotated), and pose (frontal, profile). Facial expression, occlusion, and lighting conditions also change the overall appearance of face. The Image registration algorithm will register all these images present in the database. The face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is to be implemented. Taking a pattern classification approach, each pixel in an image can be considered as a coordinate in a high-dimensional space. The advantage of this is that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space—if the face is a Lambertian surface. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing images will deviate from this linear subspace. Rather than explicitly modeling this deviation, project the image into a subspace in such a manner which discounts those regions of the face with large deviation. This is achieved by using dimension reduction techniques like Principal component analysis (PCA), Linear Discriminant analysis (LDA), Laplacian faces and other modified approaches like A Priori Laplacian and A Priori Laplacian with Hamming Distance.
References
- Gaurav Gupta and Vishal Gupta "A Priori Laplacian Concept: An Enhanced method for face recognition". International Journal of Advanced Research in Computer Science, Volume 2, No. 4, July-August 2011.
- A. M. Martinez and A. C. Kak, "PCA versus LDA," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, Feb. 2001.
- M. Turk and A. P. Pentland, "Face Recognition Using Eigenfaces," IEEE Conf. Computer Vision and Pattern Recognition, 1991.
- X. He and P. Niyogi, "Locality Preserving Projections," Proc. Conf. Advances in Neural Information Processing Systems, 2003.
- Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong-Jiang Zhang, Fellow, IEEE, "Face Recognition Using Laplacianfaces ," IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 27, no. pp 328-340, March 2005.
- R. Chellappa, C. L. Wilson and S. Sirohey, "Human and Machine Recognition of Faces: A Survey", Proceedings of the IEEE, vol. 83, no. 5, May 1995.
- K. Etemad, and R. Chellappa, "Face Recognition Using Discriminant Eigenvectors", pp. 2148-2151, IEEE, 1996.
- W. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J. Weng, "Discriminant Analysis of Principal Components for Face Recognition", pp. 336-341, International Conference on Automatic Face and Gesture Recognition, 1998.
- R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification", John Wiley & Sons, 2nd Edition, 2001.
- Face Database from AT&T Laboratories Cambridge http://www. cl. cam. ac. uk/Research/DTG/attarchive:pub/data/att_faces. zip.
- B. Srinivasa Reddy and B. N. Chatterji, "An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration", IEEE Trans. on Image Processing, Vol. 5, No. 8, August 1996 pp 1266-1271.
- Jiarui Lin, Zhiyong Gao, Bangquan Xu, Yangxiezi Cao, Zhan yingjian, "The Affection of Grey Levels on Mutual Information Based Medical Image Registration", Proceedings of the 26th Annual International Conference of the IEEE EMBS San Francisco, CA, USA September 1-5, 2004 pp 1747-1750.
- A. X. Guan, and H. H. Szu, "A Local Face Statistics Recognition Methodology beyond ICA and/or PCA", pp. 1016-1027, IEEE, 1999.
- P. J. Phillips, "Support Vector Machines Applied to Face Recognition", pp. 803-809, Advances in Neural Information Processing Systems 11, MIT Press, 1999.
- C. Podilchuk, and X. Zhang, "Face Recognition Using DCT Based Feature Vectors", pp. 2144-2147, IEEE, 1996.
- Shashua, A. Levin, and S. Avidan, "Manifold Pursuit: A New Approach to Appearance Based Recognition," Proc. Int'l Conf. Pattern Recognition, Aug. 2002.
- Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. On Pattern Analysis And Machine Intelligence, Vol. 19, No. 7, pp 711-720 July 1997.
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