Ear Biometrics- An Alternative Biometric
Description
This paper is one of the parts of a biometric based identity verification security system development project. Today, the most successful biometric based identification technologies such as fingerprint and iris scan are used worldwide in both criminal investigations and high security facilities. Even though Face recognition is one of the developing biometric methods; illumination, makeup, posing, emotional expressions and face-lifting reduce the success of face recognition. A new biometric which is not effected by any of the factors above is needed. The alternative biometric should overcome the drawbacks of face recognition. Twins are identical but their ears differ from each other, ear is also 3-dimensional but it is simpler than face and emotional expressions do not affect the ear. In the light of this, ear is a good alternative to face, as a biometric. In this study, the methods presented in the literature are tested on ear images. These methods are linear classification algorithms that work on 2D image databases. It is found out that, PCA, FLD, modified FLD which is also known as DCVA and LPP has better results at ear recognition than face recognition. Ear recognition has higher hit rates, when compared with face recognition researches that are presented in the literature previously. The results of this study proved that ear is the best alternative to face at personal identification tasks. This record was migrated from the OpenDepot repository service in June, 2017 before shutting down.
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References
- [1] Karimi B., 2006. Comparative Analysis of Face Recognition Algorithms and Investigation on the Significance of Color, M. Sc. Thesis, Concordia University, Montreal, Quebec. [2] Duda R. O., Hart P. E. and Stork D. G., 2001. Pattern Classification, John Wiley & Sons, Inc., New York. [3] Turk M. A. and Pentland A. P., 1991. Face Recognition using Eigenfaces, IEEE Conference on Computer Vision and Pattern Recognition, pp.586-591 [4] Çevikalp H., Neamtu M., Wilkes M. and Barkana A, 2005. Discriminative Common Vectors for Face Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no 1, pp. 4-13. [5] He X., Yan S., Hu Y., Niyogi P. and Zhang H. J., 2005. Face Recognition Using Laplacianfaces, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 27 no. 3, pp. 328-340. [6] He X. and Niyogi P., 2003. Locality Preserving Projections, Proc. Conf.Advances in Neural Information Processing Systems. [7] Chang K., Bowyer K. W., Sarkar S. and Victor B., 2003. Comparison and Combination of Ear and Face Images in Appearance-Based Biometrics, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 25 no. 9, pp. 1160-1165. [8] Victor B., Bowyer K. W. and Sarkar S., 2002. An Evaluation of Face and Ear Biometrics, Proc. Int'l. Conf. Pattern Recognition, pp. 429-432. [9] Hurley D. J., Nixon M. S. and Carter J. N., 2005. Forced Field Feature Extraction for Ear Biometrics, Computer Vision and Image Understanding, 98, pp. 491-512. [10] Choras M., 2005. Ear Biometrics Based on Geometrical Feature Extraction, Electronic Letters on Computer Vision and Image Analysis, 5(3), pp.84-95. [11] Balaji, Prasanalakshmi and Kannammal, Biometric Methods - A Secure Survey (October 25, 2009). Available at SSRN: https://ssrn.com/abstract=1493905. [12] S. M. S. Islam, M. Bennamoun, R. Owens, and R. Davies. Biometric approaches of 2d-3d ear and face: A survey. In T. Sobh, editor, Advances in Computer and Information Sciences and Engineering, pages 509–514. Springer Netherlands, 2008. [13] M. Choras. Image feature extraction methods for ear biometrics–a survey. In Computer Information Systems and Industrial Management Applications, 2007. CISIM '07. 6th International Conference on, pages 261 –265, june 2007. [14] K. Pun and Y. Moon. Recent advances in ear biometrics. In Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on, pages 164 – 169, May 2004. [15] H.-K. Lammi. Ear biometics. Technical report, Lappeenranta University of Technology, Department of Information Technology, 2004. [16] R. Raghavendra, K. Raja, A. Pflug, B. Yang, and C. Busch. 3d face reconstruction and multimodal person identification from video captured using a smartphone camera. In Proceedings of the 13th IEEE Conference on Technologies for Homeland Security (HST), November 2013.