Sistema de verificação de impressão digital baseado em DWT, extração de recursos de vários domínios e classificador de subespaço de conjunto

Autores

DOI:

https://doi.org/10.36561/ING.23.4

Palavras-chave:

Verificação de impressão digital, Processamento de imagem, Aprendizado de classificação, extração de recursos, Precisão, Classificador de subespaço de conjunto

Resumo

Este documento descreve um sistema de verificação de impressão digital que inclui pré-processamento, transformada Wavelet, extração de recursos usando vários domínios e classificador discriminante de subespaço de conjunto. O sistema é implementado em MATLAB usando Wavelet Toolbox, Image Processing Toolbox e Statistics and Machine Learning Toolbox. A motivação e a novidade são apresentadas primeiro, seguidas pela revisão do trabalho anterior. Todas as etapas são descritas em detalhes a seguir. Três bancos de dados de impressões digitais da literatura são usados. O desempenho do método proposto é comparado com técnicas do estado da arte baseadas em diferentes classificadores que utilizam a métrica de precisão. O algoritmo proposto atinge uma alta precisão de 97,5% para o subconjunto DB3-FVC2000.

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Referências

S. Bharkad and M. Kokare, "Fingerprint identification — ideas, influences, and trends of new age," in Pattern Recognition, Machine Intelligence and Biometrics, Berlin, Springer, 2011, pp. 411-446.

P. Schuch, "Deep learning for fingerprint recognition systems," NTNU, 2019.

A. M. Bazen, Fingerprint Identification: Feature Extraction, Matching, and Database Search, Twente University Press, 2002.

National Research Council of the National Academies, "Biometric recognition: Challenges and opportunities," National Academies Press, Washington, 2010.

S. Suwarno and P. I. Santosa, "Simple verification of low-resolution fingerprint using non-minutiae feature," in Journal of Physics: Conference Series, 2019.

M. G. Abdul-Haleem, L. E. George and H. M. Al-Bayti, "Fingerprint recognition using haar wavelet transform and local ridge attributes only," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 1, pp. 122-130, 2014.

A. Velapure and R. Talware, "Performance analysis of fingerprint recognition using machine learning algorithms," in Proc. of the Third International Conference on Computational Intelligence and Informatics, 2020.

M. Leghari, S. Memon, F. Sahito, A. A. Chandio and M. Leghari, "Biometric verification enhancement with ensemble learning classifiers," in 2018 5th International Multi-Topic ICT Conference (IMTIC), 2018.

A. Rojas and D. G. Jovanovic, "Fingerprint recognition based on wavelet transform and ensemble subspace classifier," in 2021 IEEE URUCON, Montevideo, 2021.

L. T. Nguyen, H. T. Nguyen, A. D. Afanasiev and T. V. Nguyen, "Automatic identification fingerprint based on machine learning method," Journal of the Operations Research Society of China, 2021.

M. A. Yaman, A. Subasi and F. Rattay, "Comparison of random subspace and voting ensemble machine learning methods for face recognition," Symmetry, vol. 10, no. 11, p. 651, 2018.

A. S. Ashour, Y. Guo, A. R. Hawas and G. Xu, "Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images," Health Information Science and Systems, vol. 6, 2018.

K. Radhika and S. Varadarajan, "Ensemble subspace discriminant classification of satellite images," Journal of Scientific and Industrial Research, vol. 77, pp. 633-638, 2018.

S. Basheer, K. K. Nagwanshi, S. Bhatia, S. Dubey and G. R. Sinha, "FESD: An approach for biometric human footprint matching using fuzzy ensemble learning," IEEE Access, vol. 9, pp. 26641-26663, 2021.

M. I. Islam, N. Begum, M. Alam and M. R. Amin, "Fingerprint detection using canny filter and DWT, a new approach," Journal of Information Processing Systems, vol. 6, no. 4, pp. 511-520, 2010.

M. Tico, E. Immonen, P. Ramo, P. Kuosmanen and J. Saarinen, "Fingerprint recognition using wavelet features," in ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems, 2001.

K. S. Jeyalakshmi and T. Kathirvalavakumar, "Haralick features from wavelet domain in recognizing fingerprints using neural network," in International Conference on Mining Intelligence and Knowledge Exploration, 2020.

O. Iloanusi, N. David, C. Osuagwu and S. Olisa, "Multiple domains and transform-based features for fingerprint matching," International Journal of Scientific and Technology Research, vol. 7, no. 9, pp. 27-34, 2018.

M. D. Al-Hassani, A. Kadhim and V. W. Samawi, "Fingerprint identification technique based on wavelet-bands selection features (WBSF)," International Journal of Computer Engineering and Technology (IJCET), vol. 4, no. 3, pp. 308-323, 2013.

T. Tang, "Fingerprint recognition using wavelet domain features," in 2012 8th International Conference on Natural Computation (ICNC 2012), 2012.

S. Akbar, A. Ahmad and M. Hayat, "Identification of fingerprint using discrete wavelet transform in conjunction with support vector machine," IJCSI International Journal of Computer Science Issues, vol. 11, no. 5, pp. 189-199, 2014.

H. M. Jirandeh, H. Sadeghi and M. A. Javadi Rad, "High-resolution automated fingerprint recognition system (AFRS) based on gabor wavelet and SVM," International Journal of Scientific and Engineering Research, vol. 5, no. 5, pp. 166-169, 2014.

BioLab - University of Bologna, "FVC2000," 2000. [Online]. Available: http://bias.csr.unibo.it/fvc2000/. [Accessed 4 July 2022].

BioLab - University of Bologna, "FVC2002 - Second International Fingerprint Verification Competition," 2002. [Online]. Available: http://bias.csr.unibo.it/fvc2002/. [Accessed 4 July 2022].

Biometric System Lab - University of Bologna, "FVC2004 - Third International Fingerprint Verification Competition," 2003. [Online]. Available: http://bias.csr.unibo.it/fvc2004/. [Accessed 4 July 2022].

L. Hong, Y. Wan and A. Jain, "Fingerprint image enhancement: Algorithm and performance evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777-789, 1998.

P. Kovesi, "MATLAB and Octave Functions for Computer Vision and Image Processing," 2000. [Online]. Available: https://www.peterkovesi.com/matlabfns/#fingerprints. [Accessed 17 February 2021].

A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using gabor filters," Pattern Recognition, vol. 24, no. 12, pp. 1167-1186, 1991.

B. Pathak and D. Barooah, "Texture analysis based on the gray-level co-occurrence matrix considering possible orientations," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, no. 9, pp. 4206-4212, 2013.

R. C. Gonzalez, R. E. Woods and S. L. Eddins, Digital Image Processing Using MATLAB, Gatesmark Publishing, 2009.

P. Xanthopoulos, P. Pardalos and T. Trafalis, "Linear discriminant analysis," in Robust Data Mining, Springer, 2013, pp. 27-33.

T. K. Ho, "The random subspace method for constructing decision forests," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.

MathWorks, "Ensemble Algorithms," The MathWorks, Inc., [Online]. Available: https://www.mathworks.com/help/stats/ensemble-algorithms.html#mw_58afb400-8bf3-448f-aaf2-2a2e749a3faa. [Accessed 6 April 2021].

Publicado

2022-12-21

Como Citar

[1]
A. Rojas e G. Jovanovic Dolecek, “Sistema de verificação de impressão digital baseado em DWT, extração de recursos de vários domínios e classificador de subespaço de conjunto”, Memoria investig. ing. (Facultad Ing., Univ. Montev.), nº 23, p. 32–49, dez. 2022.

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