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Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems

(2020) IEEE ACCESS. 8. p.124236-124253
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Abstract
Latent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex data-dependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces the fastest methodology for latent fingerprint identification maintaining high accuracy called Asynchronous processing for Latent Fingerprint Identification (ALFI). ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case.
Keywords
VERIFICATION, Biometrics, CUDA, fingerprint recognition, forensics, GPU, heterogeneous, computing, latent fingerprint identification, matching, minutiae, parallel processing

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MLA
Sanchez-Fernandez, Andres J., et al. “Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems.” IEEE ACCESS, vol. 8, 2020, pp. 124236–53, doi:10.1109/ACCESS.2020.3005476.
APA
Sanchez-Fernandez, A. J., Romero, L. F., Peralta, D., Medina-Perez, M. A., Saeys, Y., Herrera, F., & Tabik, S. (2020). Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems. IEEE ACCESS, 8, 124236–124253. https://doi.org/10.1109/ACCESS.2020.3005476
Chicago author-date
Sanchez-Fernandez, Andres J., Luis F. Romero, Daniel Peralta, Miguel Angel Medina-Perez, Yvan Saeys, Francisco Herrera, and Siham Tabik. 2020. “Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems.” IEEE ACCESS 8: 124236–53. https://doi.org/10.1109/ACCESS.2020.3005476.
Chicago author-date (all authors)
Sanchez-Fernandez, Andres J., Luis F. Romero, Daniel Peralta, Miguel Angel Medina-Perez, Yvan Saeys, Francisco Herrera, and Siham Tabik. 2020. “Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems.” IEEE ACCESS 8: 124236–124253. doi:10.1109/ACCESS.2020.3005476.
Vancouver
1.
Sanchez-Fernandez AJ, Romero LF, Peralta D, Medina-Perez MA, Saeys Y, Herrera F, et al. Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems. IEEE ACCESS. 2020;8:124236–53.
IEEE
[1]
A. J. Sanchez-Fernandez et al., “Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems,” IEEE ACCESS, vol. 8, pp. 124236–124253, 2020.
@article{8671769,
  abstract     = {{Latent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex data-dependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces the fastest methodology for latent fingerprint identification maintaining high accuracy called Asynchronous processing for Latent Fingerprint Identification (ALFI). ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case.}},
  author       = {{Sanchez-Fernandez, Andres J. and Romero, Luis F. and Peralta, Daniel and Medina-Perez, Miguel Angel and Saeys, Yvan and Herrera, Francisco and Tabik, Siham}},
  issn         = {{2169-3536}},
  journal      = {{IEEE ACCESS}},
  keywords     = {{VERIFICATION,Biometrics,CUDA,fingerprint recognition,forensics,GPU,heterogeneous,computing,latent fingerprint identification,matching,minutiae,parallel processing}},
  language     = {{eng}},
  pages        = {{124236--124253}},
  title        = {{Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems}},
  url          = {{http://dx.doi.org/10.1109/ACCESS.2020.3005476}},
  volume       = {{8}},
  year         = {{2020}},
}

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