Analysis of selected methods of person identification based on biometric data

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.