A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition
Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-03-01
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| Series: | International Journal of Applied Mathematics and Computer Science |
| Subjects: | |
| Online Access: | https://doi.org/10.34768/amcs-2023-0002 |
| _version_ | 1797848508295806976 |
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| author | Karlupia Namrata Mahajan Palak Abrol Pawanesh Lehana Parveen K. |
| author_facet | Karlupia Namrata Mahajan Palak Abrol Pawanesh Lehana Parveen K. |
| author_sort | Karlupia Namrata |
| collection | DOAJ |
| description | Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5 % is obtained for FR. |
| first_indexed | 2024-04-09T18:28:45Z |
| format | Article |
| id | doaj.art-79aa36d3529e406abd17187190e874ec |
| institution | Directory Open Access Journal |
| issn | 2083-8492 |
| language | English |
| last_indexed | 2024-04-09T18:28:45Z |
| publishDate | 2023-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | International Journal of Applied Mathematics and Computer Science |
| spelling | doaj.art-79aa36d3529e406abd17187190e874ec2023-04-11T17:28:19ZengSciendoInternational Journal of Applied Mathematics and Computer Science2083-84922023-03-01331213110.34768/amcs-2023-0002A Genetic Algorithm Based Optimized Convolutional Neural Network for Face RecognitionKarlupia Namrata0Mahajan Palak1Abrol Pawanesh2Lehana Parveen K.31Department of Computer Science and Information Technology, University of Jammu, Baba Saheb Ambedkar Road, 180006, Jammu, India1Department of Computer Science and Information Technology, University of Jammu, Baba Saheb Ambedkar Road, 180006, Jammu, India1Department of Computer Science and Information Technology, University of Jammu, Baba Saheb Ambedkar Road, 180006, Jammu, India2Department of Electronics, University of Jammu, Baba Saheb Ambedkar Road, 180006, Jammu, IndiaFace recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5 % is obtained for FR.https://doi.org/10.34768/amcs-2023-0002convolutional neural networkhyperparametersgenetic algorithmdeep learningevolutionary techniques |
| spellingShingle | Karlupia Namrata Mahajan Palak Abrol Pawanesh Lehana Parveen K. A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition International Journal of Applied Mathematics and Computer Science convolutional neural network hyperparameters genetic algorithm deep learning evolutionary techniques |
| title | A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition |
| title_full | A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition |
| title_fullStr | A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition |
| title_full_unstemmed | A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition |
| title_short | A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition |
| title_sort | genetic algorithm based optimized convolutional neural network for face recognition |
| topic | convolutional neural network hyperparameters genetic algorithm deep learning evolutionary techniques |
| url | https://doi.org/10.34768/amcs-2023-0002 |
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