A study of key issues in parallel algorithms for face recognition based on genetic neural networks
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 genet...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0762 |
| _version_ | 1797229601363066880 |
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| author | Guo Kai Li Biao Li Hao Bai Zhi |
| author_facet | Guo Kai Li Biao Li Hao Bai Zhi |
| author_sort | Guo Kai |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T15:15:11Z |
| format | Article |
| id | doaj.art-a5f54b5f94de4689b3922790feba6f97 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-04-24T15:15:11Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-a5f54b5f94de4689b3922790feba6f972024-04-02T09:28:42ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0762A study of key issues in parallel algorithms for face recognition based on genetic neural networksGuo Kai0Li Biao1Li Hao2Bai Zhi31School of Mechanical and Electrical Engineering, Suzhou University, Suzhou, Anhui, 234000, China.1School of Mechanical and Electrical Engineering, Suzhou University, Suzhou, Anhui, 234000, China.1School of Mechanical and Electrical Engineering, Suzhou University, Suzhou, Anhui, 234000, China.1School of Mechanical and Electrical Engineering, Suzhou University, Suzhou, Anhui, 234000, China.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.https://doi.org/10.2478/amns-2024-0762face recognitiongenetic neural networksgenetic algorithmsrobustness97n80 |
| spellingShingle | Guo Kai Li Biao Li Hao Bai Zhi A study of key issues in parallel algorithms for face recognition based on genetic neural networks Applied Mathematics and Nonlinear Sciences face recognition genetic neural networks genetic algorithms robustness 97n80 |
| title | A study of key issues in parallel algorithms for face recognition based on genetic neural networks |
| title_full | A study of key issues in parallel algorithms for face recognition based on genetic neural networks |
| title_fullStr | A study of key issues in parallel algorithms for face recognition based on genetic neural networks |
| title_full_unstemmed | A study of key issues in parallel algorithms for face recognition based on genetic neural networks |
| title_short | A study of key issues in parallel algorithms for face recognition based on genetic neural networks |
| title_sort | study of key issues in parallel algorithms for face recognition based on genetic neural networks |
| topic | face recognition genetic neural networks genetic algorithms robustness 97n80 |
| url | https://doi.org/10.2478/amns-2024-0762 |
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