Abstract
This paper compares the results of the optimization techniques for feature selection of face recognition system in which face as a biometric template gives a large domain of features for optimizing feature selection. We attempt to minimize the number of features necessary for recognition while increasing the recognition accuracy. It presents the application of differential evolution and genetic algorithm for feature subset selection. We are using local directional pattern (LDP), an extended approach of local binary patterns (LBP), to extract features. Then, the results of DE and GA are compared with the help of an extension of support vector machine (SVM) which works for multiple classes. It is used for classification. The work is performed on 10 images of ORL database resulting in better performance of differential evolution.
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References
Jain, A.K., Russ, A., Probhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14, 4–20
Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society, Kluwer Academic Publishers (1999)
Zhao, W., et al.: Face recognition: a literature survey. ACM Comput. Surv. 35, 399–458 (2003)
Ramadan, R.M., Abdel-Kader, R.F.: Face recognition using particle swarm optimization-based selected features. Int. J. Signal Process., Image Process. Pattern Recogn. 2(2) (2009)
Kumar, D., Kumar, S., Rai, C.S.: Feature selection for face recognition: a memetic algorithmic approach. J. Zhejanga Univ. Sci. A 10(8), 1140–1152 (2009)
Jin, Y., Lu, J., Ruan, Q.: Coupled discriminative feature learning for heterogeneous face recognition. IEEE Trans. Inf. Forensics Secur. 10, 640–652 (2015)
Gaynor, P., Coore, D.: Distributed face recognition using collaborative judgement aggregation in a swarm of tiny wireless sensor nodes. In: IEEE conference publication, 1–6 (2015)
Radhika, M., Apoorva, G., Nidhi, C.: Secure authentication using biometric templates in kerberos. In: 2nd International Conference on Sustainable Global Development (INDIAcom), IEEE, (2015)
Alford, A., Hansen, C., Dozier, G., Bryant, K., Kelly, J., Adams, J., Abegaz, T., Ricanek, K., Woodard, D.L.: GEC-based multi-biometric fusion. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC), New Orleans, LA, (2011)
Alford, A., Shelton, J., Dozier, G., Bryant, K., Kelly, J., Adams, J., Abegaz, T., Ricanek, K., Woodard, D.L.: A comparision of GEC-based feature selection and weighting for multimodal biometric recognition. In: Proceedings of IEEE, (2011)
Jabid, T., Kabir, M.H., Chae, O.S.: Local directional pattern (LDP) for face recognition. In: IEEE International Conference on Consumer Electronics, Jan 2010
Samanta, S.: Genetic algorithm: an approach for optimization (using MATLAB). Int. J. Latest Trends Eng. Technol. (IJLTET) 3 (2014). ISSN: 2278-621X
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic approach for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)
Jabid, T., Kabir, M.H., Chae, O.S.: Facial expression recognition using local directional pattern (LDP). In: IEEE International Conference on Image Processing, Sept 2010
Samaria, F., Harter, A.: Parameterization of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, Dec 1994
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Radhika Maheshwari, Manoj Kumar, Sushil Kumar (2016). Optimization of Feature Selection in Face Recognition System Using Differential Evolution and Genetic Algorithm. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_34
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DOI: https://doi.org/10.1007/978-981-10-0451-3_34
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