Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification
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 met...
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/5/2605 |
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| author | Ahmed Jawad A. AlBdairi Zhu Xiao Ahmed Alkhayyat Amjad J. Humaidi Mohammed A. Fadhel Bahaa Hussein Taher Laith Alzubaidi José Santamaría Omran Al-Shamma |
| author_facet | Ahmed Jawad A. AlBdairi Zhu Xiao Ahmed Alkhayyat Amjad J. Humaidi Mohammed A. Fadhel Bahaa Hussein Taher Laith Alzubaidi José Santamaría Omran Al-Shamma |
| author_sort | Ahmed Jawad A. AlBdairi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:46:55Z |
| format | Article |
| id | doaj.art-a4a566ae360942ae8aba3a7fbe55850d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:55Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a4a566ae360942ae8aba3a7fbe55850d2023-11-23T22:43:27ZengMDPI AGApplied Sciences2076-34172022-03-01125260510.3390/app12052605Face Recognition Based on Deep Learning and FPGA for Ethnicity IdentificationAhmed Jawad A. AlBdairi0Zhu Xiao1Ahmed Alkhayyat2Amjad J. Humaidi3Mohammed A. Fadhel4Bahaa Hussein Taher5Laith Alzubaidi6José Santamaría7Omran Al-Shamma8College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaCollege of Technical Engineering, The Islamic University, Najaf 54001, IraqControl and Systems Engineering Department, University of Technology-Iraq, Baghdad 00964, IraqCollege of Computer Science and Information Technology, University of Sumer, Rifai 64005, IraqCollege of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, ChinaSchool of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, AustraliaDepartment of Computer Science, University of Jaén, 23071 Jaén, SpainAlNidhal Campus, University of Information Technology & Communications, Baghdad 10001, IraqIn 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.https://www.mdpi.com/2076-3417/12/5/2605face recognitionethnicity identificationdeep learningreal-timeHPCFPGA |
| spellingShingle | Ahmed Jawad A. AlBdairi Zhu Xiao Ahmed Alkhayyat Amjad J. Humaidi Mohammed A. Fadhel Bahaa Hussein Taher Laith Alzubaidi José Santamaría Omran Al-Shamma Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification Applied Sciences face recognition ethnicity identification deep learning real-time HPC FPGA |
| title | Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification |
| title_full | Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification |
| title_fullStr | Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification |
| title_full_unstemmed | Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification |
| title_short | Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification |
| title_sort | face recognition based on deep learning and fpga for ethnicity identification |
| topic | face recognition ethnicity identification deep learning real-time HPC FPGA |
| url | https://www.mdpi.com/2076-3417/12/5/2605 |
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