Abstract
This article addresses the development of a biometric personal identification system based on palm parameter analysis using machine learning technologies. The relevance of the study is determined by the need to ensure information security, access control, and reliable user identification. Since the palm as a biometric identifier possesses a high degree of individuality, its anatomical features–such as finger contours, skin texture, palm lines, and blood vessel patterns–are used as effective characteristics for personal recognition. In the course of the study, the ROI-LANet model was employed to automatically identify significant biometric regions in palm images. This model enables accurate localization of key palm points, image alignment, and extraction of regions of interest (ROI). User classification based on the extracted ROI was performed using the deep convolutional neural network ResNet18. The residual block-based architecture allows efficient recognition of complex biometric features and improves classification accuracy. Experimental results demonstrated that the proposed system achieves high accuracy and stability. During testing, the identification accuracy reached approximately 97%. In addition, the system showed robustness to variations in lighting conditions and image acquisition angles. The obtained results indicate a high potential for practical application of the developed biometric system in security systems, access control, and transaction verification.