Analysis of selected methods of person identification based on biometric data
Artykuł w czasopiśmie
MNiSW
20
Lista 2024
| Status: | |
| Autorzy: | Rudzki Marcin , Powroźnik Paweł |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 2 |
| Wolumen/Tom: | 15 |
| Strony: | 51 - 56 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 27 czerwca 2025 |
| Abstrakty: | angielski |
| The article explores the challenge of identifying individuals using biometric data through advanced deep learning methods. The research employs three ground-breaking convolutional neural network architectures: ResNet50, EfficientNetB0, and VGG16. The project's objective was to examine the influence of critical factors, such as image quality and data processing techniques, on the performance of face identification systems. A series of experiments were carried out based on predefined test scenarios, allowing for the verification of hypotheses regarding the effects of input image resolution and data transformations on model accuracy. The experimental results highlight the substantial impact of both the chosen architecture and processing parameters on the system's identification accuracy. The article presents valuable conclusions that can inform the further development of biometric systems. Notably, the EfficientNetB0 model achieved the best performance across various metrics, including the confusion matrix and activation heatmaps, demonstrating its superior capability in identifying biometric data from facial images. |
