Abstract
To render the face recognition work more efficiently, ACOSVM, a face recognition system combining Ant Colony Optimization (ACO) with Support Vector Machine (SVM), is presented, which employs SVM classifier with the optimal features selected by ACO. The Principal Component Analysis method (PCA) is used to extract eigenfaces from images at the preprocessing stage, and then ACO for selection of the optimal subset features using cross-validation is described, which can be considered as wrapper approach in the feature selection algorithms. The experiments indicate that the proposed face recognition system with selected features is more practical and efficient when compared with others. And the results also suggest that it may find wide applications in the pattern recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proceedings of the IEEE 83(5), 705–741 (1995)
Barrett, W.A.: A survey of face recognition algorithms and testing results. In: Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers, pp. 301–305 (1997)
Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems Man, and Cybernetics, Part B 26(1), 9–41 (1996)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artificial Life 5(2), 137–172 (1999)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Inspiration for optimization from social insect behaviour. Nature 406, 39–42 (2000)
Yan, Z., Yuan, C.: Ant colony optimization for navigating complex labyrinths. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 445–448. Springer, Heidelberg (2003)
Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1470–1477 (1999)
Burges, C.J.C.: A Tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Zhang, L., Lin, F., Zhang, B.: Support vector machine learning for image retrieval. In: Proceedings of 2001 International Conference on Image Processing, pp. 721–724 (2001)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. Proceedings of the IEEE Transactions on Neural Networks 13, 415–425 (2002)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines, Software (2001), available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: 2nd IEEE Workshop on Applications of Computer Vision, Sarasota (Florida) (December 1994)
Kim, K.I., Jung, K., Kim, H.J.: Face recognition using kernel principal component analysis. IEEE Signal Processing Letters 9(2), 40–42 (2002)
Kim, K.I., Kim, J., Jung, K.: Recognition of facial images using support vector machines. In: Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing, pp. 468–471 (2001)
Yank, Z., Chongqing, L.: Face recognition using kernel principal component analysis and genetic algorithms. In: Proceedings of the 2002 12th IEEE Workshop on Neural Networks for Signal Processing, pp. 337–343 (2002)
Lawrence, S., Giles, C.L., Tsoi, A.C., Back, A.D.: Face recognition: a convolutional neural-network approach. IEEE Transactions on Neural Networks 8(1), 98–113 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, Z., Yuan, C. (2004). Ant Colony Optimization for Feature Selection in Face Recognition. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-25948-0_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22146-3
Online ISBN: 978-3-540-25948-0
eBook Packages: Springer Book Archive