Email this article 
Open Access
Subscription or Fee Access
A Hybird Particle Swarm/Nelder-Mead Clustering Algorithm for Face Recognition
Abstract
This paper presents a face recognition system that uses a hybrid clustering algorithm. The proposed clustering algorithm combines Particle Swarm Optimization (PSO) and Nelder-Mead nsearch method. The Nelder-Mead search scheme is implemented to improve the search process in the PSO algorithm. The proposed system starts by computing the Eigenfaces for all faces in the database and then these Eigenfaces are used to compute the Eigen weight vectors as feature vectors. Next, PSO algorithm is applied to group the input feature vectors into clusters. The Nelder Mead search is integrated in the PSO clustering algorithm to efficiently converge to a global optimum solution. Finally, the matching distances between the input face and the predefined clusters are detected, the minimum distance indicates the input sample cluster. A set of experiment is conducted on two datasets, Indian database and European database. The results show that the proposed scheme outperforms the recognition performance of the Eigenface solution and improves significantly the K-means clustering algorithm.
Keywords
References
M. A. Turk and A. P. Pentland. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991.
Thomas Heseltine, Nick Pears, Jim Austin, and Zezhi Chen. Z.: Face recognition: A comparison of appearance-based approaches. In In Proc. VIIth Digital Image Computing: Techniques and Applications, 2003.
Wen-Yi Zhao, Rama Chellappa, P. J. Phillips, and Azriel Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, 35:399–458, 2003.
Zhang Yankun and Liu Chongqing. Face recognition using kernel principal component analysis and genetic algorithms. In Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on, pages 337 – 343, 2002.
R.M. Ramadan and R.F. Abdel-Kader. Face Recognition Using Particle Swarm Optimization-Based Selected Features. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2(2):51–66, 2009.
Jin Wei, Zhang Jian-qi, and Zhang Xiang. Face recognition method based on support vector machine and particle swarm optimization. Expert Systems with Applications, 38(4):4390 – 4393, 2011.
Raghavendra, R., Dorizzi, B., Rao, A., & Kumar, G. H. (2011). Particle swarm optimization based fusion of near infrared and visible images for improved face verification. Pattern Recognition, 44(2), 401-411.
Villegas, O. O. V., Quintero, M. A., Sanchez, V. G. C., & Dominguez, H. D. J. O. (2009). A Novel Evolutionary Face Recognition Algorithm Using Particle Swarm Optimization. 2009 Fifth International Conference on Signal Image Technology and Internet Based Systems, 42-47.
A. M. Bagirov, A. M. Rubinov, N.V. Soukhoroukova, and J. Yearwood. Unsupervised and supervised data classification via nonsmooth and global optimization. Top, 11:1–93, 2003.
D. W. van der Merwe and Andries Petrus Engelbrecht. Data clustering using particle swarm optimization. In IEEE Congress on Evolutionary Computation (1), pages 215–220, 2003.
Ching-yi Cheo. Particle Swarm Optimization Algorithm and Its Application to Clustering Analysis. Change, (1):789–794, 2004.
J. A. Nelder and R. Mead. A Simplex Method for Function Minimization. The Computer Journal, 7(4):308–313, January 1965.
Praveen Koduru, Sanjoy Das, and Stephen Welch. A particle swarm optimization-nelder mead hybrid algorithm for balanced exploration and exploitation in multidimensional search space. In IC-AI, pages 457–464, 2006.
J. Kennedy and R. Eberhart. Particle swarm optimization. volume 4, pages 1942–1948 vol.4, 1995.
Vidit Jain and Amitabha Mukherjee. The indian face database, 2002.
L.Spacek D.Hond. Distinctive descriptions for face processing. pages pp.320–329, England, 1997).
Refbacks
- There are currently no refbacks.