2023
DOI: 10.11591/ijeecs.v29.i2.pp1166-1173
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A parallel algorithm of multiple face detection on multi-core system

Abstract: <p><span lang="EN-US">This work offers a graphics processing unit (GPU)-based system for real-time face recognition, which can detect and identify faces with high accuracy. This work created and implemented novel parallel strategies for image integral, computation scan window processing, and classifier amplification and correction as part of the face identification phase of the Viola-Jones cascade classifier. Also, the algorithm and parallelized a portion of the testing step during the facial recog… Show more

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Cited by 3 publications
(2 citation statements)
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“…The authors found that the system achieves a confidence rate of 0.75 and above, making it effective in identifying individuals with a history of felonies based on facial images. Al-Neama et al build a GPUbased system for real-time face recognition [70]. According to the findings, the system significantly outperforms traditional CPU-based methods, with a 19.72x improvement in the detection phase and a remarkable 1573x improvement in the recognition phase when implemented on an NVidia GTX 570 graphics card.…”
Section: Ingroup Biasmentioning
confidence: 99%
“…The authors found that the system achieves a confidence rate of 0.75 and above, making it effective in identifying individuals with a history of felonies based on facial images. Al-Neama et al build a GPUbased system for real-time face recognition [70]. According to the findings, the system significantly outperforms traditional CPU-based methods, with a 19.72x improvement in the detection phase and a remarkable 1573x improvement in the recognition phase when implemented on an NVidia GTX 570 graphics card.…”
Section: Ingroup Biasmentioning
confidence: 99%
“…The "Network Common Data Form" NetCDF files (.nc) that has been developed by Unidata [7] since 1989 is a commonly used format for distributing and exchanging climate, environmental, and bioinformatics dataset [8,9]. It contains specific scientific data with multiple dimensions such as time, location, and other variables [10].…”
Section: Introductionmentioning
confidence: 99%