Abstract
This paper offers an approach to biometric analysis using ears for recognition. The ear has all the assets that a biometric trait should possess. Because it is a study field in potential growth, this study offers an approach using SURF features as an input of a neural network with the purpose to detect and recognize a person by the patterns of its ear, also includes, the development of an application with .net to show experimental results of the theory applied. Ear characteristics, which are a unchanging biometric approach that does not vary with age, have been used for several years in the forensic science of recognition, thats why the research gets important value in the present. To perform this study, we worked with the help of Police School of Ávila, Province of Spain, we have built a database with approximately 300 ears.
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References
Kumar, A., Hanmandlu, M., Kuldeep, M., Gupta, H.M.: Automatic ear detection for online biometric applications, pp. 146–149 (2011)
Cummings, A., Nixon, M., Carter, J.: A novel ray analogy for enrolment of ear biometrics. In: BTAS 2010, pp. 1–6 (2010)
Victor, B., Bowyer, K., Sarkar, S.: An evaluation of face and ear biometrics. In: ICPR 2002, vol. 1, pp. 429–432 (2002)
Carreira-Perpinan, M.A.: Compression neural networks for feature extraction: Application to human recognition from ear images. Master’s thesis, Faculty of Informatics, Technical University of Madrid, Spain (1995)
Castrillón-Santana, M., Lorenzo-Navarro, J., Hernández-Sosa, D.: An Study on Ear Detection and Its Applications to Face Detection, pp. 313–322 (2011)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Hurley, D.J., Nixon, M.S., Carter, J.N.: Force field feature extraction for ear biometrics. Computer Vision and Image Understanding, 491–512 (2005)
Heaton, J.: Introduction to Neural Networks for C#, 2nd edn. (2010), http://www.heatonresearch.com
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (SURF). Computer Vision and Image Understanding, 346–359 (2008)
Jolliffe, I.T.: Principal Components Analysis, 2nd edn., New York (2002)
Bustard, J., Nixon, M.: 3D morphable model construction for robust ear and face recognition. In: CVPR 2010, pp. 2582–2589 (2010)
Krystian, M., Cordelia, S.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1615–1630 (2005)
Lin, K.-H., Lam, K.-M., Siu, W.-C.: Spatially eigen-weighted Hausdorff distances for human face recognition. Polytechnic University, Hong Kong (2002)
Kyong, C., Kevin, W.B., Sudeep, S., Barnabas, V.: Comparison and combination of ear and face images in appearance-based biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1160–1165 (2003)
Alvarez, L., Gonzalez, E., Mazorra, L.: Fitting ear contour using an ovoid model. In: Proceedings of IEEE International Carnahan Conference on Security Technology (ICCST 2005), pp. 145–148 (2005)
Yuan, L., Mu, Z.-C.: Ear detection based on skin-color and contour information. In: ICMLC 2007, vol. 4, pp. 2213–2217 (2007)
Burge, M., Burger, W.: Ear biometrics in computer vision. In: ICPR 2000, vol. 2, pp. 822–826 (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 71–86 (1991)
Yan, P., Bowyer, K.W.: Empirical evaluation of advanced ear bio-metrics. In: Conference on Computer Vision and Pattern Recognition, vol. 3, pp. 41–48 (2005)
Ansari, S., Gupta, P.: Localization of ear using outer helix curve of the ear. In: Proceedings of the International Conference on Computing: Theory and Applications (ICCTA 2007), pp. 688–692 (2007)
Attarchi, S., Faez, K., Rafiei, A.: A new segmentation approach for ear recognition. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 1030–1037. Springer, Heidelberg (2008)
Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Transactions on Image Processing 17(11), 2029–2039 (2008)
USTB Database, University of Science and Technology Beijing
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Galdámez, P.L., Arrieta, M.A.G., Ramón, M.R. (2014). A Brief Approach to the Ear Recognition Process. In: Omatu, S., Bersini, H., Corchado, J., Rodríguez, S., Pawlewski, P., Bucciarelli, E. (eds) Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-07593-8_54
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DOI: https://doi.org/10.1007/978-3-319-07593-8_54
Publisher Name: Springer, Cham
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