<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition

Department of Computer Science and Information Technology

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition

Data publikacji: 29 mar 2023

Zakres stron: 21 - 31

Otrzymano: 15 maj 2022

Przyjęty: 20 gru 2022

DOI: https://doi.org/10.34768/amcs-2023-0002

Słowa kluczowe
convolutional neural network, hyperparameters, genetic algorithm, deep learning, evolutionary techniques

© 2023 Namrata Karlupia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Genetic Algorithm Based Optimized Convolutional Neural Network for Face Recognition

Data publikacji: 29 mar 2023

Zakres stron: 21 - 31

Otrzymano: 15 maj 2022

Przyjęty: 20 gru 2022

DOI: https://doi.org/10.34768/amcs-2023-0002

Słowa kluczoweconvolutional neural network, hyperparameters, genetic algorithm, deep learning, evolutionary techniques

© 2023 Namrata Karlupia et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
convolutional neural network, hyperparameters, genetic algorithm, deep learning, evolutionary techniques