ASSESSING THE ROBUSTNESS OF FACIAL CLASSIFICATION METHODS IN THE BIOMETRIC IDENTIFICATION AREA
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| Wydane w: | International Multidisciplinary Scientific GeoConference : SGEM vol. 2, no. 1 (2025), p. 3-12 |
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| 1. autor: | |
| Kolejni autorzy: | , , |
| Wydane: |
Surveying Geology & Mining Ecology Management (SGEM)
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| Hasła przedmiotowe: | |
| Dostęp online: | Citation/Abstract Full Text Full Text - PDF |
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| 100 | 1 | |a Hiitter, Marek |u Faculty of Safety Engineering, VSB - Technical University of Ostrava, Czech Republic | |
| 245 | 1 | |a ASSESSING THE ROBUSTNESS OF FACIAL CLASSIFICATION METHODS IN THE BIOMETRIC IDENTIFICATION AREA | |
| 260 | |b Surveying Geology & Mining Ecology Management (SGEM) |c 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a This article focuses on the effectiveness and robustness of facial classification systems in the field of biometric identification. Artificial intelligence is increasingly becoming a part of everyday life, with more and more users employing it across various domains. In the field of security, Al is used, for instance, in cybersecurity and risk analysis. It is also integrated into surveillance systems, particularly for facial recognition. A comparative analysis of three convolutional neural networks-GoogLeNet, ResNet-101, and DenseNet-201-was conducted in this study using the MATLAB simulation environment. These CNNs were pre-trained and subsequently tested from several perspectives, including performance, training time, and validation accuracy. The collected data served as a basis for comparing the networks with one another and were also used for further analysis of training and output evaluation. The results can form the basis for further research and can be compared with a possible study in which real photographs with higher noise were used. The results can also be applied to enhance electronic security systems, such as access control for mines and geologically significant sites. | |
| 653 | |a Face recognition | ||
| 653 | |a Accuracy | ||
| 653 | |a Classification systems | ||
| 653 | |a Comparative analysis | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Biometrics | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Neural networks | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Training | ||
| 653 | |a Facial recognition technology | ||
| 653 | |a Risk analysis | ||
| 653 | |a Access control | ||
| 653 | |a Biometric identification | ||
| 653 | |a Robustness | ||
| 653 | |a Data collection | ||
| 653 | |a Business metrics | ||
| 653 | |a Security systems | ||
| 653 | |a Biometry | ||
| 653 | |a Algorithms | ||
| 653 | |a Surveillance | ||
| 653 | |a Surveillance systems | ||
| 653 | |a Personal appearance | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Holubova, Véra |u Faculty of Safety Engineering, VSB - Technical University of Ostrava, Czech Republic | |
| 700 | 1 | |a Ščurek, Radomir |u Faculty of Safety Engineering, VSB - Technical University of Ostrava, Czech Republic | |
| 700 | 1 | |a Lukaštik, Jaroslav |u Faculty of Safety Engineering, VSB - Technical University of Ostrava, Czech Republic | |
| 773 | 0 | |t International Multidisciplinary Scientific GeoConference : SGEM |g vol. 2, no. 1 (2025), p. 3-12 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3275246906/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3275246906/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3275246906/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |