Acelerando a identificação de impressões digitais através da técnica de offloading computacional
Abstract
Fingerprint recognition has been widely used to control people's access to controlled environments or devices, such as smartphones and laptops. Among other problems, the resources present in smartphones and smart locks (e.g., computational power and storage) are limited and make it impossible to use them for fingerprint identification tasks where the database to be compared is numerous. In this context, this work presents a case study of the adoption of the computational offloading technique to identify people through fingerprint readers and smartphones. We present the solution architecture and the experiments with Cloud and Cloudlet's support to improve the identification process. The results demonstrate that using the offloading technique compared to the execution on the device speeds up the fingerprint identification process by at least 85%.
References
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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.
Published
2021-08-16
How to Cite
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: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (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.
