Estudo comparativo entre dois métodos de localização da fronteira externa da íris: um estudo de caso
Resumo
Uma das etapas mais importantes no sistema de reconhecimento por íris é a sua localização dentro da imagem. Este trabalho apresenta uma comparação de desempenho entre dois métodos de localização da fronteira externa da íris aplicado a uma base de dados de um conhecido benchmark. Os dois métodos selecionados foram: o Operador Intero-Diferencial de Daugman e a Transformada de Hough. A comparação foi realizada através do processo de validação cruzada para definição do intervalo de confiança. O teste t-Student emparelhado unicaudal mostrou que existe diferença de desempenho com um nível de confiança de 95%.
Referências
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Buddharpawar, A. S., , and Subbaraman, S. (2015). Iris recognition based on pca for person identification. International Journal of Computer Applications, NCESC 2015(1):9–12.
D. Zhao, W. Luo, R. L. and Yue, L. (2018). Negative iris recognition. IEEE Transactions on Dependable and Secure Computing, 15(15):112–125.
da Costa, R. M. and Gonzaga, A. (2012). Dynamic features for iris recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(4):1072– 1082.
Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):21–30.
Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148–1161.
Duda, R. O. and Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11–15.
G. J. Bergues, L. Canali, C. S. and Flesia, A. G. (2015). Sub-pixel gray-scale hough transform for an electronic visual interface. IEEE Latin America Transactions, 13(9):3135– 3141.
G. J. Mohammed, H. B. R. and Al-Kazzaz, A. A. (2009). A new localization algorithm for iris recognition. Information Technology Journal, 8(2):226–230.
Jain, A. K. and Nandakumar, K. (2012). Biometric authentication: System security and user privacy. IEEE Computer, 45(11):87–92.
Montgomery, D. C. and Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
M.Shamsi, P. B. Saad, S. B. I. and Kenari, A. R. (2009). Fast algorithm for iris localization using daugman circular intero-differential operator. In International Conference of Soft Computing and Pattern Recognition, pages 1–10.
O. C. Abikoye, J. S. Sadiku, K. S. A. and Jimoh, R. G. (2014). Iris feature extraction for personal identification using fast wavelet transform (fwt). International Journal of Applied Information Systems, 6(9):1–10.
R. Y. F. Ng, Y. H. T. and Mok, K. M. (2008). A review of iris recognition algorithms. In Proceedings of the International Symposium on Information Technology, pages 1–7.
Roselin, V. andWaghmare, L. M. (2013). Pupil detection and feature extraction algorithm for iris recognition. In Proceedings of the AMO-Advanced Modeling and Optimization, pages 1–12.
S. A. Sudiro, I. P. Wardhani, B. A. W. and Handias, B. (2017). Fingerprint matching application using hardware based artificial neural network with matlab. In Proceedings of the 5th International Conference on Electrical, Electronics and Information Engineering, pages 66–77.
Sanpachai, H. and Settapong, M. (2015). A study of image enhancement for iris recognition. Journal of Industrial and Intelligent Information, 3(1):61–64.
Sarode, N. S. and Patil, A. (2015). Iris recognition using lbp with classifiers-knn and nb. International Journal of Science and Research, 4(1):1–10.
U. Gawande, M. Z. and Kapur, A. (2010). Improving iris recognition accuracy by score based fusion method. International Journal of Advancements in Technology, 1(1):1–8.
Y. Li, Y. Li, K. X. Q. Y. and Deng, R. H. (2018). Empirical study of face authentication systems under osnfd attacks. IEEE Transactions on Dependable and Secure Computing, 15(2):231–245.
Buddharpawar, A. S., , and Subbaraman, S. (2015). Iris recognition based on pca for person identification. International Journal of Computer Applications, NCESC 2015(1):9–12.
D. Zhao, W. Luo, R. L. and Yue, L. (2018). Negative iris recognition. IEEE Transactions on Dependable and Secure Computing, 15(15):112–125.
da Costa, R. M. and Gonzaga, A. (2012). Dynamic features for iris recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(4):1072– 1082.
Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):21–30.
Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148–1161.
Duda, R. O. and Hart, P. E. (1972). Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11–15.
G. J. Bergues, L. Canali, C. S. and Flesia, A. G. (2015). Sub-pixel gray-scale hough transform for an electronic visual interface. IEEE Latin America Transactions, 13(9):3135– 3141.
G. J. Mohammed, H. B. R. and Al-Kazzaz, A. A. (2009). A new localization algorithm for iris recognition. Information Technology Journal, 8(2):226–230.
Jain, A. K. and Nandakumar, K. (2012). Biometric authentication: System security and user privacy. IEEE Computer, 45(11):87–92.
Montgomery, D. C. and Runger, G. C. (2010). Applied statistics and probability for engineers. John Wiley & Sons.
M.Shamsi, P. B. Saad, S. B. I. and Kenari, A. R. (2009). Fast algorithm for iris localization using daugman circular intero-differential operator. In International Conference of Soft Computing and Pattern Recognition, pages 1–10.
O. C. Abikoye, J. S. Sadiku, K. S. A. and Jimoh, R. G. (2014). Iris feature extraction for personal identification using fast wavelet transform (fwt). International Journal of Applied Information Systems, 6(9):1–10.
R. Y. F. Ng, Y. H. T. and Mok, K. M. (2008). A review of iris recognition algorithms. In Proceedings of the International Symposium on Information Technology, pages 1–7.
Roselin, V. andWaghmare, L. M. (2013). Pupil detection and feature extraction algorithm for iris recognition. In Proceedings of the AMO-Advanced Modeling and Optimization, pages 1–12.
S. A. Sudiro, I. P. Wardhani, B. A. W. and Handias, B. (2017). Fingerprint matching application using hardware based artificial neural network with matlab. In Proceedings of the 5th International Conference on Electrical, Electronics and Information Engineering, pages 66–77.
Sanpachai, H. and Settapong, M. (2015). A study of image enhancement for iris recognition. Journal of Industrial and Intelligent Information, 3(1):61–64.
Sarode, N. S. and Patil, A. (2015). Iris recognition using lbp with classifiers-knn and nb. International Journal of Science and Research, 4(1):1–10.
U. Gawande, M. Z. and Kapur, A. (2010). Improving iris recognition accuracy by score based fusion method. International Journal of Advancements in Technology, 1(1):1–8.
Y. Li, Y. Li, K. X. Q. Y. and Deng, R. H. (2018). Empirical study of face authentication systems under osnfd attacks. IEEE Transactions on Dependable and Secure Computing, 15(2):231–245.
Publicado
09/04/2019
Como Citar
NETO, Delmiro; NETO, Rosalvo.
Estudo comparativo entre dois métodos de localização da fronteira externa da íris: um estudo de caso. In: ESCOLA REGIONAL DE COMPUTAÇÃO BAHIA, ALAGOAS E SERGIPE (ERBASE) , 2019, Ilhéus.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
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p. 225-234.
