Automated Multimodal Biometrics Using Face and Ear

In this paper, we present an automated multimodal biometric system for the detection and recognition of humans using face and ear as input. The system is totally automated, with a trained detection system for face and for ear. We look at individual recognition rates for both face and ear, and then at combined recognition rates, and show that an automated multimodal biometric system achieves significant performance gains. We also discuss methods of combining biometric input and the recognition rates that each achieves.

[1]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[3]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[4]  Kevin W. Bowyer,et al.  Multibiometrics Using Face and Ear , 2008 .

[5]  Sudeep Sarkar,et al.  Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[8]  Zhi-Chun Mu,et al.  Multimodal recognition based on face and ear , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.

[9]  Jun Qin,et al.  A SVM face recognition method based on Gabor-featured key points , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[10]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[11]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.