Abstract
This paper presents a multimodal biometric system for face and ear biometrics which convolves face and ear images with Gabor wavelet filters for extracting enhanced Gabor features from the corresponding images which are characterized by spatial frequency, spatial locality and orientation. Gaussian Mixture Model (GMM) is applied to the Gabor responses for measurements and Expectation Maximization algorithm is used to estimate density parameters in GMM. It produces two sets of feature sets which are fused using Support Vector Machines. Experiments on two different databases reveal its usefulness towards robust multimodal fusion.
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References
Jain, A.K., Ross, A.K.: Multibiometric Systems. Communications of the ACM 47(1), 34–40 (2004)
Rattani, A., Kisku, D.R., Bicego, M., Tistarelli, M.: Robust Feature-Level Multibiometric Classification. In: Proceedings of the Biometric Consortium Conference- A special issue in Biometrics, pp. 1–6 (2006)
Brunelli, R., Falavigna, D.: Person Identification using Multiple Cues. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(10), 955–966 (1995)
Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)
Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Fusion of Face and Speech Data for Person Identity Verification. IEEE Transactions on Neural Networks 10(5), 1065–1075 (1999)
Ross, A., Jain, A.K.: Information Fusion in Biometrics. Pattern Recognition Letters 24(13), 2115–2125 (2003)
Lee, T.S.: Image Representation using 2D Gabor Wavelets. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 959–971 (1996)
Bredin, H., Dehak, N., Chollet, G.: GMM-based SVM for Face Recognition. In: IEEE International Conference on Pattern Recognition, pp. 1111–1114 (2006)
Gutschoven, B., Verlinde, P.: Multi-modal Identity Verification using Support Vector Machines (SVM). In: Proceedings of the 3rd International Conference on Information Fusion (2000)
Smeraldi, F., Capdevielle, N., Bigün, J.: Facial Features Detection by Saccadic Exploration of the Gabor Decomposition and Support Vector Machines. In: 11th Scandinavian Conference on Image Analysis, pp. 39–44 (1999)
Iannarelli, A.: Ear Identification. In: Forensic Identification series, Paramont Publishing Company, Fremont (1989)
Chang, K., Bowyer, K.W., Sarkar, S.: Comparison and Combination of Ear and Face Images in Appearance- based Biometrics. IEEE Transaction on Pattern Analysis and Machine Intelligence 25(9), 1160–1165 (2003)
Kisku, D.R., Rattani, A., Grosso, E., Tistarelli, M.: Face Identification by SIFT-based Complete Graph Topology. In: 5th IEEE International Workshop on Automatic Identification Advanced Technologies, pp. 63–68 (2007)
Carreira-Perpiñán, M.A.: Compression Neural Networks for Feature Extraction: Application to Human Recognition from Ear Images. M.Sc. Thesis, Faculty of Informatics, Technical University of Madrid, Spain (1995)
Kisku, D.R., Mehrotra, H., Gupta, P., Sing, J.K.: Probabilistic Graph-based Feature Fusion and Score Fusion using SIFT Features for Face and Ear Biometrics. In: International Symposium on Optics and Photonics, vol. 7443, 744306 (2009)
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Kisku, D.R., Gupta, P., Sing, J.K., Nasipuri, M. (2010). Fusion of Gaussian Mixture Densities for Face and Ear Biometrics Using Support Vector Machines. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_34
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DOI: https://doi.org/10.1007/978-3-642-17569-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17568-8
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