Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 17
Year of Publication: 2012
S. Arivalagan
K. Venkatachalapathy

S Arivalagan and K Venkatachalapathy. Article: Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm. International Journal of Computer Applications 55(17):18-22, October 2012. Full text available. BibTeX

	author = {S. Arivalagan and K. Venkatachalapathy},
	title = {Article: Face Recognition based on a Hybrid Meta-heuristic Feature Selection Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {17},
	pages = {18-22},
	month = {October},
	note = {Full text available}


For the past few years, a number of new face recognition techniques have been proposed. Always it is a big unanswered question among face recognition researchers about which method or technique will have better performance. In this study an approach to recognize known faces based on Eigen vectors and a hybrid Meta-heuristic feature selection algorithm is proposed. The eigenvectors which are covariance matrix of the face images together describes the difference between face images. Face recognition problem is viewed as a two dimensional recognition problem. Initially the face images are projected in to face space and using Principal component analysis the eigenvectors with high Eigenvalues are extracted to reduce the dimension of the feature vector. Further to select the best feature vectors which increase the classification accuracy is selected by using a hybrid meta-heuristic algorithm using Genetic algorithm (GA) and Bacteria Foraging Optimization (BFO). In this study the Support vector machine (SVM) and Back propagation neural network (BPNN) are used for classification. The classifiers are trained and tested separately using the frontal face images taken from AT&T database. The SVM and BPNN produces an average classification accuracy of 82. 6% and 83. 28% respectively.


  • Dimitri PISSARENKO. Eigen face –based facial Recognition. Dec 1, 2002.
  • Pramod kumar pandey, Yaduvir singh, Swetha tripathi. International Journal of Computer Applications (0975-8887). vol. 15 No. 4. February 2011.
  • Zakariya, S. M. , Ali, R. Automatic Face reconition system by combining four individual algorithms. Computational Intelligence and Communication Networks (CICN) International Conference 2011, Pages: 222 - 226.
  • Cong Geng, Xudong Jiang. Face recognition using SIFT features. ICIP'09 Proceedings of the 16th IEEE international conference on Image processing. Pages: 3277-3280 .
  • Donghyun kim, Hyeyoung park. An efficient face recognition through combining local features and statistical feature extraction. PRICAI'10 Proceedings of the 11th Pacific Rim International conference on trends in artificial intelligence. Pages: 456-466, Springer.
  • Hiremath, S. , Joshi, D. G. , Chadda, V. K. , Bajpai, A. A multi-algorithmic face recognition algorithm. Advanced Computing and Communications, 2006. International Conference 2006, Page(s): 321 – 326.
  • C. -J. Tu, L. -Y. Chuang, J. -Y. Chang, and C. -H. Yang. Feature Selection using PSO-SVM. International Journal of Computer Science (IAENG), vol. 33, no. 1, IJCS_33_1_18.
  • E. Kokiopoulou and P. Frossard. Classification-Specific Feature Sampling for Face Recognition. Proc IEEE 8th Workshop on Multimedia Signal Processing, pp. 20-23, 2006.
  • A. Y. Yang, J. Wright,Y. Ma, and S. S. Sastry. Feature Selection in Face Recognition: A Sparse Representation Perspective, 2007.
  • X. Fan and B. Verma. Face recognition: a new feature selection and classification technique. Proc. 7th Asia-Pacific Conference on Complex Systems, December 2004.
  • D. -S. Kim, I. -J. Jeon, S. -Y. Lee, P. -K. Rhee, and D. -J. Chung. Embedded Face Recognition based on Fast Genetic Algorithm for Intelligent Digital Photography. IEEE Trans. Consumer Electronics, vol. 52, no. 3, pp. 726-734, August 2006.
  • M. L. Raymer, W. F. Punch, E. D. Goodman, L. A. Kuhn, and A. K Jain. Dimensionality Reduction Using Genetic Algorithms. IEEE Trans. Evolutionary Computation, vol. 4, no. 2, pp. 164-171, July 2000.
  • H. R. Kanan, K. Faez, and M. Hosseinzadeh. Face Recognition System Using Ant Colony Optimization-Based Selected Features. Proc. IEEE Symp. Computational Intelligence in Security and Defense Applications (CISDA 2007), pp 57-62, April 2007.
  • De Jong K. Learning with Genetic Algorithms:An overview. Machine Learning vol. 3, Kluwer Academic publishers, 1988.
  • Haleh Vafaie and Kenneth De Jong. Genetic Algorithms as a Tool for Feature Selection in Machine Learning. Center for Artificial Intelligence, George Mason University.
  • Vafaie, H. , and De Jong, K. A. Improving the performance of a Rule Induction System Using Genetic Algorithms. Proceedings of the First International Workshop on MULTISTRATEGY LEARNING, Harpers Ferry, W. Virginia, USA, 1991.
  • J. Adler (1966). Chemotaxis in bacteria. Science, vol. 153, pp. 708–716.
  • Passino K. M. Biomimicr of bacteria foraging for optimization and control. Control systems, IEEE. Vol. 22 Issue 3. Page(s): 52-67.
  • H. Chen, Y. Zhu and R. Hu. Cooperative Bacterial Foraging Optimization. Research Article, Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, China, 2009.
  • Ahmed Al-Ani. Ant colony optimization for Feature subset selection. Proceedings of World Academy of Science, Engineering And Technology, February 2005, vol. 4 ISSN 1307-6884.
  • Rasleen Jakhar, Navdeep Kaur, Ramandeep Singh. Face Recognition Using Bacteria Foraging Optimization-Based Selected Features. (IJACSA) International Journal of Advanced Computer Science and Applications, Page(s): 106-111.
  • O. Ludwig and U. Nunes. Novel Maximum-Margin Training Algorithms for Supervised Neural Networks. IEEE Transactions on Neural Networks, vol. 21, issue 6, pp. 972-984, Jun. 2010.
  • Z. Sun, X. Yuan, G. Bebis, S. Louis, Neural-network-based gender classi1cation using genetic eigen-feature extraction, IEEE International Joint Conference on Neural Networks, May 2002.