IIM  Vol.1 No.3 , December 2009
Multimodal Belief Fusion for Face and Ear Biometrics
ABSTRACT
This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on two multimodal databases, namely, IIT Kanpur database and virtual database. Former contains face and ear images of 400 individuals while later consist of both images of 17 subjects taken from BANCA face database and TUM ear database. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.

Cite this paper
nullD. KISKU, P. GUPTA, H. MEHROTRA and J. SING, "Multimodal Belief Fusion for Face and Ear Biometrics," Intelligent Information Management, Vol. 1 No. 3, 2009, pp. 166-171. doi: 10.4236/iim.2009.13024.
References
[1]   A. K. Jain and A. K. Ross, “Multibiometric systems,” Communications of the ACM, Vol. 47, No.1, pp. 34–40, 2004.

[2]   A. K. Jain, A. K. Ross, and S. Prabhakar, “An introduction to biometrics recognition,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp. 4–20, 2004.

[3]   A. Rattani, D. R. Kisku, M. Bicego, and M. Tistarelli, “Robust feature-level multibiometric classification,” Proceedings of the Biometric Consortium Conference – A special issue in Biometrics, pp. 1–6, 2006.

[4]   D. R. Kisku, A. Rattani, E. Grosso, and M. Tistarelli, “Face identification by SIFT-based complete graph topology,” Proceedings of the IEEE Workshop on Automatic Identification Advanced Technologies, pp. 63–68, 2007.

[5]   B. Arbab-Zavar, M. S. Nixon, and D. J. Hurley, “On model-based analysis of ear biometrics,” First IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–5, 2007.

[6]   K. Chang, K. W. Bowyer, S. Sarkar, and B. Victor “Comparison and combination of ear and face images in appearance-based biometrics,” Transaction on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1160–1165, 2003.

[7]   N. Wilson, Algorithms for Dempster-Shafer theory, Oxford Brookes University.

[8]   T. S. Lee, “Image representation using 2D Gabor wavelets,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 18, pp. 959–971, 1996.

[9]   L. Xu and M. I. Jordan, “On convergence properties of the EM algorithm for Gaussian Mixtures,” Neural Computation, Vol. 8, No. 1, pp. 129–151, 1996.

[10]   F. Smeraldi, N. Capdevielle, and J. Bigün, “Facial features detection by saccadic exploration of the gabor decomposition and support vector machines,” In the 11th Scandinavian Conference on Image Analysis, pp. 39–44, 1999.

[11]   A. Iannarelli, Ear Identification, Forensic Identification series, Fremont, Paramont Publishing Company, California, 1989.

[12]   M. A. Carreira-Perpi?án, “Compression neural networks for feature extraction: Application to human recognition from ear images,” MSc thesis, Faculty of Informatics, Technical University of Madrid, Spain, 1995.

[13]   D. R. Kisku, M. Tistarelli, J. K. Sing, and P. Gupta, “Face recognition by fusion of local and global matching scores using DS theory: An evaluation with uni-classifier and multi-classifier paradigm,” In the Proceedings of IEEE Computer Soceity Conference on Computer Vision and Pattern Recognition Workshop, pp. 60–65, 2009.

 
 
Top