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Article

Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification

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College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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Department of Computer Science, University of Babylon, Babylon 51001, Iraq
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College of Technical Engineering, The Islamic University, Najaf 54001, Iraq
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Control and Systems Engineering Department, University of Technology-Iraq, Baghdad 00964, Iraq
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College of Computer Science and Information Technology, University of Sumer, Rifai 64005, Iraq
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School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Department of Computer Science, University of Jaén, 23071 Jaén, Spain
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AlNidhal Campus, University of Information Technology & Communications, Baghdad 10001, Iraq
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Authors to whom correspondence should be addressed.
Academic Editors: Monica Perusquia Hernandez and Antonio Fernández-Caballero
Appl. Sci. 2022, 12(5), 2605; https://doi.org/10.3390/app12052605
Received: 7 December 2021 / Revised: 26 February 2022 / Accepted: 1 March 2022 / Published: 2 March 2022
In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources. View Full-Text
Keywords: face recognition; ethnicity identification; deep learning; real-time; HPC; FPGA; GPU face recognition; ethnicity identification; deep learning; real-time; HPC; FPGA; GPU
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MDPI and ACS Style

AlBdairi, A.J.A.; Xiao, Z.; Alkhayyat, A.; Humaidi, A.J.; Fadhel, M.A.; Taher, B.H.; Alzubaidi, L.; Santamaría, J.; Al-Shamma, O. Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification. Appl. Sci. 2022, 12, 2605. https://doi.org/10.3390/app12052605

AMA Style

AlBdairi AJA, Xiao Z, Alkhayyat A, Humaidi AJ, Fadhel MA, Taher BH, Alzubaidi L, Santamaría J, Al-Shamma O. Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification. Applied Sciences. 2022; 12(5):2605. https://doi.org/10.3390/app12052605

Chicago/Turabian Style

AlBdairi, Ahmed J.A., Zhu Xiao, Ahmed Alkhayyat, Amjad J. Humaidi, Mohammed A. Fadhel, Bahaa H. Taher, Laith Alzubaidi, José Santamaría, and Omran Al-Shamma. 2022. "Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification" Applied Sciences 12, no. 5: 2605. https://doi.org/10.3390/app12052605

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