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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohammed Wajid Al-Neama
Mosul University

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A parallel algorithm of multiple face detection on multi-core system Mohammed Wajid Al-Neama; Abeer A. Mohamad Alshiha; Mustafa Ghanem Saeed
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 2: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i2.pp1166-1173

Abstract

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 recognition stage were experimented with. The suggested approach significantly improves existing facial recognition methods by enhancing the performance of two crucial components. The experimental findings show that the proposed method, when implemented on an NVidia GTX 570 graphics card, outperforms the typical CPU program by a factor of 19.72 in the detection phase and 1573 in the recognition phase, with only 2000 images trained and 40 images tested. The recognition rate will plateau when the hardware's capabilities are maxed out. This demonstrates that the suggested method works well in real-time.
Biometric face recognition method using graphics processing unit system Abeer A. Mohamad Alshiha; Mohammed Wajid Al-Neama; Abdalrahman R. Qubaa
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp183-191

Abstract

The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.
Co-Authors Abdalrahman R. Qubaa Abeer A. Mohamad Alshiha Mustafa Ghanem Saeed