Acelerando a identificação de impressões digitais através da técnica de offloading computacional
Resumo
O reconhecimento de impressões digitais tem sido amplamente utilizado para liberar o acesso de pessoas a ambientes controlados ou dispositivos, como smartphones e notebooks. Dentre outros problemas, os recursos presentes em smartphones e fechaduras inteligentes (e.g., poder computacional e armazenamento) são limitados e inviabilizam a utilização destes para tarefas de identificação onde a base de dados a ser comparada é numerosa. Nesse contexto, este trabalho apresenta um estudo de caso da adoção da técnica de offloading computacional para a identificação de pessoas através do uso de leitores de impressões digitais e smartphones. São apresentados a arquitetura da solução e os experimentos realizados com apoio da Nuvem e Cloudlet para acelerar o processo de identificação. Os resultados são promissores e indicam que a técnica de offloading reduz o tempo de identificação de impressões digitais em pelo menos 85% quando comparado à execução no dispositivo móvel.
Referências
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Costa, P. B., Rego, P. A. L., Rocha, L. S., Trinta, F. A., and de Souza, J. N. (2015). Mpos: A multiplatform ofoading system. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 577–584. ACM.
Hassan, G. and Elgazzar, K. (2016). The case of face recognition on mobile devices. In 2016 IEEE Wireless Communications and Networking Conference, pages 1–6.
Kumar, K., Liu, J., Lu, Y.-H., and Bhargava, B. (2013). A survey of computation ofoading for mobile systems. Mobile Networks and Applications, 18(1):129–140.
Moses, K. R., Higgins, P., McCabe, M., Prabhakar, S., and Swann, S. (2011). Automated ngerprint identication system (as). Scientic Working Group on Friction Ridge Analysis Study and Technology and National institute of Justice (eds.) SWGFAST-The ngerprint sourcebook, pages 1–33.
Peralta, D., Triguero, I., Sanchez-Reillo, R., Herrera, F., and Benítez, J. M. (2014). Fast ngerprint identication for large databases. Pattern Recognition, 47(2):588–602.
Powers, N., Alling, A., Osolinsky, K., Soyata, T., Zhu, M., Wang, H., Ba, H., Heinzelman, W., Shi, J., and Kwon, M. (2015). The cloudlet accelerator: Bringing mobile-cloud In 2015 IEEE Globecom Workshops (GC Wkshps), face recognition into real-time. pages 1–7.
Ratha, N. and Bolle, R. (2003). Automatic ngerprint recognition systems. Springer Science & Business Media.
Rego, P. A., Costa, P. B., Coutinho, E. F., Rocha, L. S., Trinta, F. A., and de Souza, J. N. (2017). Performing computation ofoading on multiple platforms. Computer Communications, 105:1–13.
Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A. K., Elhoseny, M., and Baik, S. W. (2020). Raspberry pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Generation Computer Systems, 108:995 – 1007.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4):14–23.
Xiao, Q. and Raafat, H. (1991). Fingerprint image postprocessing: a combined statistical and structural approach. Pattern Recognition, 24(10):985–992.
Xu, J., Jiang, J., Dou, Y., and Shen, X. (2014). A low-cost fully pipelined architecture for ngerprint matching. In 2014 12th International Conference on Signal Processing (ICSP), pages 413–418. IEEE.
Zaeri, N. (2011). Minutiae-based ngerprint extraction and recognition. Biometrics.
Zhao, Y.-x., Zhang, W.-x., Li, D.-s., Huang, Z., Li, M.-n., and Lu, X.-c. (2016). Pegasus: a distributed and load-balancing ngerprint identication system. Frontiers of Information Technology & Electronic Engineering, 17(8):766–780.
Barbosa, R. A. and Rego, P. A. L. (2019). A mobile cloud computing testbed based on lightweight virtualization. In 2019 IEEE Global Communications Conference (GLOBECOM), pages 1–6.
Bordel, B., Alcarria, R., Robles, T., and Martín, D. (2017). Cyber–physical systems: Extending pervasive sensing from control theory to the internet of things. Pervasive and Mobile Computing, 40:156–184.
Costa, P. B., Rego, P. A. L., Rocha, L. S., Trinta, F. A., and de Souza, J. N. (2015). Mpos: A multiplatform ofoading system. In Proceedings of the 30th Annual ACM Symposium on Applied Computing, pages 577–584. ACM.
Hassan, G. and Elgazzar, K. (2016). The case of face recognition on mobile devices. In 2016 IEEE Wireless Communications and Networking Conference, pages 1–6.
Kumar, K., Liu, J., Lu, Y.-H., and Bhargava, B. (2013). A survey of computation ofoading for mobile systems. Mobile Networks and Applications, 18(1):129–140.
Moses, K. R., Higgins, P., McCabe, M., Prabhakar, S., and Swann, S. (2011). Automated ngerprint identication system (as). Scientic Working Group on Friction Ridge Analysis Study and Technology and National institute of Justice (eds.) SWGFAST-The ngerprint sourcebook, pages 1–33.
Peralta, D., Triguero, I., Sanchez-Reillo, R., Herrera, F., and Benítez, J. M. (2014). Fast ngerprint identication for large databases. Pattern Recognition, 47(2):588–602.
Powers, N., Alling, A., Osolinsky, K., Soyata, T., Zhu, M., Wang, H., Ba, H., Heinzelman, W., Shi, J., and Kwon, M. (2015). The cloudlet accelerator: Bringing mobile-cloud In 2015 IEEE Globecom Workshops (GC Wkshps), face recognition into real-time. pages 1–7.
Ratha, N. and Bolle, R. (2003). Automatic ngerprint recognition systems. Springer Science & Business Media.
Rego, P. A., Costa, P. B., Coutinho, E. F., Rocha, L. S., Trinta, F. A., and de Souza, J. N. (2017). Performing computation ofoading on multiple platforms. Computer Communications, 105:1–13.
Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A. K., Elhoseny, M., and Baik, S. W. (2020). Raspberry pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Generation Computer Systems, 108:995 – 1007.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE pervasive Computing, 8(4):14–23.
Xiao, Q. and Raafat, H. (1991). Fingerprint image postprocessing: a combined statistical and structural approach. Pattern Recognition, 24(10):985–992.
Xu, J., Jiang, J., Dou, Y., and Shen, X. (2014). A low-cost fully pipelined architecture for ngerprint matching. In 2014 12th International Conference on Signal Processing (ICSP), pages 413–418. IEEE.
Zaeri, N. (2011). Minutiae-based ngerprint extraction and recognition. Biometrics.
Zhao, Y.-x., Zhang, W.-x., Li, D.-s., Huang, Z., Li, M.-n., and Lu, X.-c. (2016). Pegasus: a distributed and load-balancing ngerprint identication system. Frontiers of Information Technology & Electronic Engineering, 17(8):766–780.
Publicado
16/08/2021
Como Citar
BARBOSA, Renan A.; ANDRADE, João P. B.; SILVA, Mauro R. C. da; LIMA, Francisco I. da S.; TRINTA, Fernando A. M.; REGO, Paulo A. L..
Acelerando a identificação de impressões digitais através da técnica de offloading computacional. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 29-42.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16709.
