Um Guia Prático para Matching de Impressões Digitais
Resumo
O reconhecimento biométrico por impressões digitais é amplamente utilizado devido à unicidade dessas estruturas. Apesar de ser uma área consolidada, iniciantes e desenvolvedores ainda enfrentam dificuldades decorrentes da ausência de diretrizes claras, da dispersão da documentação e da baixa legibilidade de parte dos trabalhos de referência. Para mitigar esse problema, este trabalho apresenta um guia prático e estruturado sobre o fluxo de funcionamento de um Sistema Automatizado de Identificação de Impressões Digitais (AFIS) baseado em minúcias. O pipeline metodológico compreende quatro etapas: aquisição de imagens, pré-processamento para redução de ruídos, extração de minúcias e comparação (matching) com uma base de dados. Também são discutidas limitações de compatibilidade entre ferramentas de referência, como o software MINDTCT e o algoritmo Minutiae Cylinder-Code (MCC), além da disponibilidade de bases de dados públicas. A estrutura proposta busca reduzir a curva de aprendizado e facilitar o desenvolvimento de novos sistemas biométricos, contribuindo para aplicações como identificação de pessoas desaparecidas, refugiadas, foragidas, sem documentação ou em situação de rua, combate a fraudes em programas sociais e acesso a serviços de saúde pública.
Palavras-chave:
Impresões Digitais, Reconhecimento Biométrico, Minúcias
Referências
Bakheet, S., Al-Hamadi, A., e Youssef, R. (2022a). A fingerprint-based verification framework using harris and surf feature detection algorithms. Applied Sciences, 12(4):2028.
Bakheet, S., Alsubai, S., Alqahtani, A., e Binbusayyis, A. (2022b). Robust fingerprint minutiae extraction and matching based on improved sift features. Applied Sciences, 12(12):6122.
Biometric System Laboratory (2026). Research activities. [link]. Accessed: 2026-05-26.
Cappelli, R. e Ferrara, M. (2012). A fingerprint retrieval system based on level-1 and level-2 features. Expert Systems with Applications, 39(12):10465–10478.
Cappelli, R., Ferrara, M., Franco, A., e Maltoni, D. (2007). Fingerprint verification competition 2006. Biometric Technology Today, 15(7-8):7–9.
Cappelli, R., Ferrara, M., e Maltoni, D. (2010). Minutia cylinder-code: A new representation and matching technique for fingerprint recognition. IEEE transactions on pattern analysis and machine intelligence, 32(12):2128–2141.
Gibb, C. e Riemen, J. (2023). Toward better afis practice and process in the forensic fingerprint environment. Forensic Science International: Synergy, 7:100336.
Liu, X., Raja, K., Wang, R., Qiu, H., Wu, H., Sun, D., Zheng, Q., Liu, N., Wang, X., Huang, G., et al. (2024). A latent fingerprint in the wild database. IEEE Transactions on Information Forensics and Security, 19:3703–3718.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., e Jain, A. K. (2002a). Fvc2000: Fingerprint verification competition. IEEE transactions on pattern analysis and machine intelligence, 24(3):402–412.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., e Jain, A. K. (2002b). Fvc2002: Second fingerprint verification competition. In 2002 International conference on pattern recognition, volume 3, pages 811–814. IEEE.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., e Jain, A. K. (2004). Fvc2004: Third fingerprint verification competition. In International conference on biometric authentication, pages 1–7. Springer.
Maltoni, D., Maio, D., Jain, A. K., e Feng, J. (2022a). Fingerprint sensing. Handbook of fingerprint recognition, pages 63–114.
Maltoni, D., Maio, D., Jain, A. K., e Feng, J. (2022b). Handbook of Fingerprint Recognition. Springer Cham, Cham, Switzerland, 3 edition.
Maltoni, D., Maio, D., Jain, A. K., e Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer.
Martins, N., Silva, J. S., e Bernardino, A. (2024). Fingerprint recognition in forensic scenarios. Sensors, 24(2):664.
Mohamed Abdul Cader, A. J., Banks, J., e Chandran, V. (2023). Fingerprint systems: sensors, image acquisition, interoperability and challenges. Sensors, 23(14):6591.
Peng, A. e Huang, R. (2025). Research progress on the application of deep learning in fingerprint recognition. Pattern Recognition, page 112216.
Peralta, D., García, S., Benitez, J. M., e Herrera, F. (2017). Minutiae-based fingerprint matching decomposition: methodology for big data frameworks. Information Sciences, 408:198–212.
Raghavan, R. e John Singh, K. (2024). An enhanced and hybrid fingerprint minutiae feature extraction method for identifying and authenticating the patient’s noisy fingerprint. International Journal of System Assurance Engineering and Management, 15(1):84–97.
Schuch, P., Schulz, S., e Busch, C. (2018). Survey on the impact of finger-print image enhancement. IET Biometrics, 7(2):102–115.
Singh, R., Singh, R., Tripathi, R. K., e Agarwal, P. (2025). Fingerprint recognition using artificial neural networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 95(2):127–135.
Tang, Y., Gao, F., Feng, J., e Liu, Y. (2017). Fingernet: An unified deep network for fingerprint minutiae extraction. In Biometrics (IJCB), 2017 IEEE International Joint Conference on. IEEE.
Vibert, B., Le Bars, J.-M., Charrier, C., e Rosenberger, C. (2023). Comparative study of minutiae selection methods for digital fingerprints. Frontiers in Big Data, 6:1146034.
Watson, C. I., Garris, M. D., Tabassi, E., Wilson, C. L., McCabe, R. M., Janet, S., e Ko, K. (2007). User’s guide to nist biometric image software (nbis).
Win, K. N., Li, K., Chen, J., Viger, P. F., e Li, K. (2020). Fingerprint classification and identification algorithms for criminal investigation: A survey. Future Generation Computer Systems, 110:758–771.
Yu, Y., Niu, Q., Li, X., Xue, J., Liu, W., e Lin, D. (2023). A review of fingerprint sensors: Mechanism, characteristics, and applications. Micromachines, 14(6):1253.
Bakheet, S., Alsubai, S., Alqahtani, A., e Binbusayyis, A. (2022b). Robust fingerprint minutiae extraction and matching based on improved sift features. Applied Sciences, 12(12):6122.
Biometric System Laboratory (2026). Research activities. [link]. Accessed: 2026-05-26.
Cappelli, R. e Ferrara, M. (2012). A fingerprint retrieval system based on level-1 and level-2 features. Expert Systems with Applications, 39(12):10465–10478.
Cappelli, R., Ferrara, M., Franco, A., e Maltoni, D. (2007). Fingerprint verification competition 2006. Biometric Technology Today, 15(7-8):7–9.
Cappelli, R., Ferrara, M., e Maltoni, D. (2010). Minutia cylinder-code: A new representation and matching technique for fingerprint recognition. IEEE transactions on pattern analysis and machine intelligence, 32(12):2128–2141.
Gibb, C. e Riemen, J. (2023). Toward better afis practice and process in the forensic fingerprint environment. Forensic Science International: Synergy, 7:100336.
Liu, X., Raja, K., Wang, R., Qiu, H., Wu, H., Sun, D., Zheng, Q., Liu, N., Wang, X., Huang, G., et al. (2024). A latent fingerprint in the wild database. IEEE Transactions on Information Forensics and Security, 19:3703–3718.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., e Jain, A. K. (2002a). Fvc2000: Fingerprint verification competition. IEEE transactions on pattern analysis and machine intelligence, 24(3):402–412.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., e Jain, A. K. (2002b). Fvc2002: Second fingerprint verification competition. In 2002 International conference on pattern recognition, volume 3, pages 811–814. IEEE.
Maio, D., Maltoni, D., Cappelli, R., Wayman, J. L., e Jain, A. K. (2004). Fvc2004: Third fingerprint verification competition. In International conference on biometric authentication, pages 1–7. Springer.
Maltoni, D., Maio, D., Jain, A. K., e Feng, J. (2022a). Fingerprint sensing. Handbook of fingerprint recognition, pages 63–114.
Maltoni, D., Maio, D., Jain, A. K., e Feng, J. (2022b). Handbook of Fingerprint Recognition. Springer Cham, Cham, Switzerland, 3 edition.
Maltoni, D., Maio, D., Jain, A. K., e Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer.
Martins, N., Silva, J. S., e Bernardino, A. (2024). Fingerprint recognition in forensic scenarios. Sensors, 24(2):664.
Mohamed Abdul Cader, A. J., Banks, J., e Chandran, V. (2023). Fingerprint systems: sensors, image acquisition, interoperability and challenges. Sensors, 23(14):6591.
Peng, A. e Huang, R. (2025). Research progress on the application of deep learning in fingerprint recognition. Pattern Recognition, page 112216.
Peralta, D., García, S., Benitez, J. M., e Herrera, F. (2017). Minutiae-based fingerprint matching decomposition: methodology for big data frameworks. Information Sciences, 408:198–212.
Raghavan, R. e John Singh, K. (2024). An enhanced and hybrid fingerprint minutiae feature extraction method for identifying and authenticating the patient’s noisy fingerprint. International Journal of System Assurance Engineering and Management, 15(1):84–97.
Schuch, P., Schulz, S., e Busch, C. (2018). Survey on the impact of finger-print image enhancement. IET Biometrics, 7(2):102–115.
Singh, R., Singh, R., Tripathi, R. K., e Agarwal, P. (2025). Fingerprint recognition using artificial neural networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 95(2):127–135.
Tang, Y., Gao, F., Feng, J., e Liu, Y. (2017). Fingernet: An unified deep network for fingerprint minutiae extraction. In Biometrics (IJCB), 2017 IEEE International Joint Conference on. IEEE.
Vibert, B., Le Bars, J.-M., Charrier, C., e Rosenberger, C. (2023). Comparative study of minutiae selection methods for digital fingerprints. Frontiers in Big Data, 6:1146034.
Watson, C. I., Garris, M. D., Tabassi, E., Wilson, C. L., McCabe, R. M., Janet, S., e Ko, K. (2007). User’s guide to nist biometric image software (nbis).
Win, K. N., Li, K., Chen, J., Viger, P. F., e Li, K. (2020). Fingerprint classification and identification algorithms for criminal investigation: A survey. Future Generation Computer Systems, 110:758–771.
Yu, Y., Niu, Q., Li, X., Xue, J., Liu, W., e Lin, D. (2023). A review of fingerprint sensors: Mechanism, characteristics, and applications. Micromachines, 14(6):1253.
Publicado
08/09/2026
Como Citar
TORRES, Letícia; PAULINO, Tamires; MONTEIRO, José Maria; MACHADO, Javam.
Um Guia Prático para Matching de Impressões Digitais. In: DATA SCIENCE FOR SOCIAL GOOD BRAZILIAN WORKSHOP (DS4SG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 593-602.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249701.
