Abstract
Gabor wavelet-based methods have been proven that are useful in many problems including face detection. It has been shown that these features tackle well facing into image recognition. In image identification, while there is a number of human faces in a repository of employees, it is aimed to identify the face of an arrived employee is which one? So the application of gabor wavelet-based features is reasonable. We propose a weighted majority average voting classifier ensemble to handle the problem. We show that the proposed mechanism works well in an employees’ repository of our laboratory.
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
Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recognition Letters 24(13), 2115–2125 (2003)
Pekalska, E., Duin, R., Skurichina, M.: A discussion on the classifier projection space for classifier combining. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 137–148. Springer, Heidelberg (2002)
Kittler. J., Li. Y.P., Matas. J., Sánchez, M.U.R.: Combining evidence in multimodal personal identity recognition systems. Audio- and Video-Based Biometric Authentication (1997)
Kittler, J., Matas, G., Jonsson, K., Sánchez, M.: Combining evidence in personal identity verification systems. Pattern Recognition Letters 18(9), 845–852 (1997)
Bilmes, J., Kirchhoff, K.: Directed graphical models of classifier combination: Application to phone recognition. Spoken Language Processing (2000)
Tax, D.M.J., Breukelen, M.V., Duin, R.P.W., Kittler, J.: Combining multiple classifiers by averaging or by multiplying. Pattern Recognition 33, 1475–1485 (2000)
Kuncheva, L.I.: Combining Pattern Classifiers, Methods and Algorithms. Wiley, New York (2005)
Singh, S., Singh, M.: A dynamic classifier selection and combination approach to image region labeling. Signal Processing: Image Communication 20, 219–231 (2005)
Štruc, V., Pavešić, N.: Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition. INFORMATICA 20(1), 115–138 (2009)
Hong, L., Jain, A., Pankanti, S., Bolle, R.: Fingerprint enhancement. In: IEEE WACV, Sarasota, pp. 202–207 (1996)
Bashar, M.K., Matsumoto, T., Ohnishi, N.: Wavelet transform-based locally orderless images for texture segmentation. Pattern Recognition Letters 24(15), 2633–2650 (2003)
Žibert, J., Mihelič, F.: Zhao–Atlas–Marks representation of speech signals. Electrotechnical Review 69(3-4), 159–164 (2002)
Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(5), 572–581 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Parvin, H., Mozayani, N., Beigi, A. (2011). A Classifier Ensemble for Face Recognition Using Gabor Wavelet Features. In: Herrero, Á., Corchado, E. (eds) Computational Intelligence in Security for Information Systems. Lecture Notes in Computer Science, vol 6694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21323-6_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-21323-6_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21322-9
Online ISBN: 978-3-642-21323-6
eBook Packages: Computer ScienceComputer Science (R0)