A study of key issues in parallel algorithms for face recognition based on genetic neural networks

被引：0
作者：
Guo K. [1 ,2 ]
Li B. [1 ,3 ]
Li H. [1 ]
Bai Z. [1 ]
机构：
[1] School of Mechanical and Electrical Engineering, Suzhou University, Anhui, Suzhou
[2] Anhui Provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot, Anhui, Wuhu
[3] School of Information and Control Engineering, China University of Mining and Technology, Jiangsu, Xuzhou
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
Face recognition; Genetic algorithms; Genetic neural networks; Robustness;
D O I：
10.2478/amns-2024-0762
中图分类号：
学科分类号：
摘要：
This study examines the effectiveness of Genetic Neural Networks (GNN) in face recognition, particularly in optimizing parallel algorithms to overcome the challenges posed by complex data. We have significantly improved recognition accuracy and computational efficiency by employing an adaptive genetic algorithm that fine-tunes neural network weights through Selection, crossover, and mutation. Our approach was tested across diverse datasets, covering variations in posture, age, ethnicity, and lighting conditions. The results demonstrate outstanding recognition rates: 99.82% on LFW, 97.94% on AgeDB-30, 95.11% on CFP-FP, 95.87% on CALFW, and 89.44% on CPLFW, showcasing exceptional robustness against complex lighting and occlusions. Additionally, our algorithm maintains balanced accuracy across different ethnicities with an overall recognition rate of 96.77% and boasts a substantial reduction in processing time to an average of 4.15 seconds. These advancements underscore the potential and practicality of our method in enhancing face recognition technology. © 2023 Kai Guo, Biao Li, Hao Li and Zhi Bai, published by Sciendo.
引用
收藏
相关论文
共 20 条
  • [1] Tang X., Ding C., Information security terminal architecture of power transportation mobile internet of things based on big data analysis, Wireless Communications and Mobile Computing, (2021)
  • [2] Zhang G., Ji X., Li Y., Xu W., Power-based non-intrusive condition monitoring for terminal device in smart grid, Sensors, 20, 13, (2020)
  • [3] Hussain M., Jain U., Simple and secure device authentication mechanism for smart environments using internet of things devices, International Journal of Communication Systems, 6, (2020)
  • [4] Okello E., Ayieko P., Kwena Z., Nanyonjo G., Bahemuka U., Price M., Et al., Acceptability and applicability of biometric iris scanning for the identification and follow up of highly mobile research participants living in fishing communities along the shores of lake victoria in kenya, tanzania, and uganda, International journal of medical informatics, 172, (2023)
  • [5] Kim D., Kima K.S., A statistical inference attack on privacy-preserving biometric identification scheme, IEEE Access, 99, (2021)
  • [6] Xu Q., Zhang C., Sun B., Emotion recognition model based on the dempster–shafer evidence theory, Journal of Electronic Imaging, 29, 2, (2020)
  • [7] Oztel I., Yolcu G., Oz, Cemil, Et al., Ifer: facial expression recognition using automatically selected geometric eye and eyebrow features, Journal of Electronic Imaging, (2018)
  • [8] Zhao X., Zhu J., Luo B., Gao Y., Survey on facial expression recognition: history, applications, and challenges, IEEE multimedia, pp. 28-34, (2021)
  • [9] Roussi A., Resisting the rise of facial recognition, Nature, 587, 7834, pp. 350-353, (2020)
  • [10] Tian J., Fang J., Wu Y., Facial expression recognition in classroom environment based on improved xception model, Journal of electronic imaging, (2022)
12