@misc{10481/63583,
year = {2020},
url = {http://hdl.handle.net/10481/63583},
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 datadependencies, 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
ALFI (Asynchronous processing for Latent Fingerprint Identification), the fastest methodology for latent
fingerprint identification maintaining high accuracy. 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 fully 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.},
organization = {Spanish Government

TIN2016-80920-R},
organization = {Spanish Government

TIN2016-80920-R},
organization = {University of Malaga through the U-Smart-Drive project},
organization = {I Plan Propio de Investigacion},
organization = {Postdoctoral Fellowship of the Research Foundation of Flanders (FWO) 	
170303/12X1619N},
organization = {ISAC Marylou Ingram Scholar},
organization = {Spanish Government

RYC-2015-18136},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {Asynchronous processing},
keywords = {Accelerator architectures},
keywords = {CUDA},
keywords = {Fine-grained parallelism},
keywords = {Fingerprint recognition},
keywords = {Heterogeneous computing},
keywords = {Latent fingerprint identification},
keywords = {Parallel processing},
title = {Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems},
doi = {10.1109/ACCESS.2020.3005476},
author = {Herrera Triguero, Francisco and Tabik, Siham and Sánchez, Andrés J.},
}