Machine Learning System for Human – Ear Recognition Using Scale Invariant Feature Transform
Abstract
Biometrics techniques are the standard of a wide group of many applications for a human’s identification and verification issues. Because of this reason, a high scale of security needs to search for a new way to identify the person arises. In this paper, establish a human ear recognition system is proposed. This system combines four main phases: ear detection, ear feature extraction, ear recognition, and confirmation. The essential of the proposed system is to divide the ear image into the skin and non-skin pixels using a likelihood skin detector. The likelihood image processes by morphological operations to complete ear regions. Scale-invariant feature transform uses for extracting the fixed features of the ear. Ear recognition includes two modes identification mode and verification mode. Euclidean Distance Measure (EDM) uses for similarity measure between the first image in the database and a new image. According to the three experiments conducted in this paper, the results of the different datasets, the accuracy ratio are 100%, 92%.and 92% respectively.
References
Abaza A., Ross A., Hebert C., Harrison M. A. F., and Nixon M., “A Survey on Ear Biometrics,” Acm Comput. Surv., 45 (2): 22, 2011.
Ali M., Javed M. Y., and Basit A., “Ear Recognition Using Wavelets,” Image Vis. Comput. New Zeal. 2007, pp. 83–86, 2007.
Anil K. Jain, Arun A. Ross, and Patrick Flynn, ''Handbook of Biometrics'', Springer Science & Business Media, LLC, 2008.
Anwar A. S., Ghany K. K. A., and Elmahdy H., “Human Ear Recognition Using Geometrical Features Extraction,” Procedia Comput. Sci., 65 (1): 529–537, 2015.
Arun A. Ross, Karthik Nandakumar, and Anil K. Jain,''Handbook of Multi Biometrics'', Springer Science & Business Media, LLC, 2006.
Ibrahim M. I. S., Nixon M. S., and Mahmoodi S., “Shaped wavelets for curvilinear structures for ear biometrics,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), 6453 (1): 499–508, 2010.
Indi T. S. and Raut S. D., “Person unique identification based on ear’s biometric features,” 2013 Int. Conf. Intell. Syst. Signal Process. ISSP 2013, pp. 128– 133, 2013.
Kisku D. R. Gupta S., Gupta P., and Sing J. K., “An efficient ear identification system,” 2010 5th Int. Conf. Futur. Inf. Technol. Futur. 2010 - Proc., pp. 0–5, 2010.
Kumar A. and Wu C., “Automated human identification using ear imaging,” Pattern Recognit., 45(3): 956–968, 2012.
Peng-YengYin,”Pattern Recognition Techniques, Technology and Applications”, I-Tech, Vienna, Austria, November 2008.
Peter Gregory and Michael A. Simon, "Biometrics for Dummies", Wiley Publishing, Inc., 2008.
Pflug A. and Busch C., “Ear biometrics: a survey of detection, feature extraction, and recognition methods,” IET Biometrics, 1 (2): 114- 129, 2012.
Prakash S. and Gupta P., “An efficient ear localization technique,” Image Vis. Comput., 30(11): 38–50, 2012.
Stan Li Z. “Encyclopedia of Biometrics: I-Z”. Vol. 1. Springer Science & Business Media, 2009.
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