Um estudo comparativo das estratégias de fusão no nível de característica para Sistemas Biométricos Multimodais baseados em face e íris
Resumo
Com o avanço da tecnologia, novas abordagens para o reconhecimento automático da identidade de uma pessoa têm sido propostas e tal fato tem encorajado o emprego de Sistemas Biométricos. Essa abordagem utiliza características físicas ou comportamentais de uma pessoa para realizar a sua identificação. Os Sistemas Biométricos podem ser classificados como Unimodais ou Multimodais. Sistemas Biométricos Unimodais utilizam apenas uma modalidade biométrica para realizar o reconhecimento, ao passo que os Sistemas Biométricos Multimodais empregam duas ou mais modalidades. A construção de um Sistema Biométrico Multimodal pode ser realizada de diferentes formas, as quais são categorizadas de acordo com a sua arquitetura, nível de fusão e estratégia de fusão. O objetivo deste trabalho é investigar diferentes formas de fusão de modalidades biométricas no nível de característica, visando projetar um sistema multimodal com alto poder de reconhecimento. Neste artigo, nós utilizamos a Transformada Wavelet para extrair um conjunto de características de imagens de face e íris. Os resultados obtidos mostram que os Sistemas Biométricos Multimodais apresentam melhor desempenho que os Sistemas Biométricos Unimodais, em termos de taxa de reconhecimento calculada sobre a saída produzida pela Máquina de Vetores Suporte usada como classificador.
Palavras-chave:
Biometria Multimodal, Biometria Unimodal, Transformada Wavelet, Máquina de Vetores Suporte
Referências
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T. Camus and R. Wildes. Reliable and fast eye finding in close-up images. In Pattern Recognition, 2002. Proceedings. 16th International Conference on, volume 1, pages 389–394 vol.1, 2002.
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V. Kabeer, T. M. Thasleema, and N. K. Narayanan. Face recognition using state space parameters and k-nn classifier. In Innovations in Information Technology, 2007. IIT ’07. 4th International Conference on, pages 476–480, Nov 2007.
V. M. Mane and D. V. Jadhav. Review of multimodal biometrics: Applications, challenges and research areas. International Journal of Biometrics and Bioinformatics (IJBB), 2009.
V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998.
V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA, 1995.
Z. Liu, L. Zhang, and L. Zhu. An improved face recognition method based on gabor wavelet transform and svm. In Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on, volume 1, pages 378–381, Oct 2012.
A. Jain, A. Ross, and S. Prabhakar. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):4–20, 2004.
A. K. Jain, P. Flynn, and A. A. Ross. Handbook of Biometrics. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2007.
A. Louren¸co, H. Silva, and A. Fred. Ecg-based biometrics: A real time classification approach. In Machine Learning for Signal Processing (MLSP), 2012 IEEE International Workshop on, pages 1–6. IEEE, 2012.
A. Louren¸co, H. Silva, and A. Fred. Ecg-based biometrics: A real time classification approach. In Proceedings of the 22nd IEEE International Workshop on Machine Learning for Signal Processing, 2012.
A. Ross and A. K. Jain. Multimodal Biometrics: an overview. pages 1221–1224, 2004.
B. Sch¨olkopf and A. J. Smola. Learning with kernels : support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. MIT Press, 2002.
C. S. C. S. Burrus, R. A. Gopinath, and H. Guo. Introduction to wavelets and wavelet transforms : a primer. Upper Saddle River, N.J. Prentice Hall, 1998.
D. M. Rankin, B. W. Scotney, P. J. Morrow, and B. K. Pierscionek. Iris recognition failure over time: The effects of texture. Pattern Recogn., 45(1):145–150, Jan. 2012.
H. F. Liau and D. Isa. Feature selection for support vector machine-based face-iris multimodal biometric system. Expert Systems with Applications, 38(9):11105 – 11111, 2011.
H. Proenca and L. Alexandre. Iris recognition: An analysis of the aliasing problem in the iris normalization stage. In Computational Intelligence and Security, 2006 International Conference on, volume 2, pages 1771–1774, 2006.
J. Daugman. How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14:21–30, 2002.
K. Dharavath, F. Talukdar, and R. Laskar. Study on biometric authentication systems, challenges and future trends: A review. In Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on, pages 1–7, Dec 2013.
L. Flom and A. Saffir. Iris recognition system. U.S Patent 4 641 394, 1987.
L. Hong and A. Jain. Integrating faces and fingerprints for personal identification. IEEE transactions on pattern analysis and machine intelligence, 20:1295–1307, 1998.
M. A. Lone, S. Zakariya, and R. Ali. Automatic face recognition system by combining four individual algorithms. In Computational Intelligence and Communication Networks (CICN), 2011 International Conference on, pages 222–226. IEEE, 2011.
M. Mazloom and S. Ayat. Combinational method for face recognition: Wavelet, pca and ann. In Digital Image Computing: Techniques and Applications (DICTA), 2008, pages 90–95, Dec 2008.
M. Mazloom, S. Kasaei, and H. A. Neissi. Construction and application of svm model and wavelet-pca for face recognition. In Proceedings of the 2009 Second International Conference on Computer and Electrical Engineering - Volume 01, ICCEE ’09, pages 391–398, Washington, DC, USA, 2009. IEEE Computer Society.
N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 1 edition, 2000.
P. Viola and M. Jones. Robust real-time face detection. International Journal of Comp. Vision, 57(2):137–154, 2004.
R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles’. The electroencephalogram as a biometric. In Electrical and Computer Engineering, 2001. Canadian Conference on, volume 2, pages 1363–1366 vol.2, 2001.
S. Mallat and S. Zhong. Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell., 14(7):710–732, 1992.
T. Camus and R. Wildes. Reliable and fast eye finding in close-up images. In Pattern Recognition, 2002. Proceedings. 16th International Conference on, volume 1, pages 389–394 vol.1, 2002.
V. Divyaloshini and M. Saraswathi. Performance evaluation of image fusion techniques and its implementation in biometric recognition. International Journal of Technology Enhancements and Emerging Engineering Research, 2(3), 2014.
V. Kabeer, T. M. Thasleema, and N. K. Narayanan. Face recognition using state space parameters and k-nn classifier. In Innovations in Information Technology, 2007. IIT ’07. 4th International Conference on, pages 476–480, Nov 2007.
V. M. Mane and D. V. Jadhav. Review of multimodal biometrics: Applications, challenges and research areas. International Journal of Biometrics and Bioinformatics (IJBB), 2009.
V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998.
V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., New York, NY, USA, 1995.
Z. Liu, L. Zhang, and L. Zhu. An improved face recognition method based on gabor wavelet transform and svm. In Computational Intelligence and Design (ISCID), 2012 Fifth International Symposium on, volume 1, pages 378–381, Oct 2012.
Publicado
26/05/2015
Como Citar
COSTA, Daniel; PASSOS, Henrique; PERES, Sarajane; LIMA, Clodoaldo.
Um estudo comparativo das estratégias de fusão no nível de característica para Sistemas Biométricos Multimodais baseados em face e íris. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 11. , 2015, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 219-226.
DOI: https://doi.org/10.5753/sbsi.2015.5820.
