Optimization of Neural Network Models of Computer Vision for Biometric Identification on Edge IoT Devices

This research is dedicated to the development of an intelligent biometric system based on the synergy of Internet of Things (IoT) technologies and Artificial Intelligence (AI). The primary goal of this research is to explore the possibilities of personal identification using two distinct biometric traits: facial images and the venous pattern of the palm. These methods are treated as independent approaches, each relying on unique anatomical features of the human body. This study analyzes state-of-the-art methods in computer vision and neural network architectures and presents experimental results related to the extraction and comparison of biometric features. For each biometric modality, specific approaches to data collection, preprocessing, and analysis are proposed. We frame optimization in practical terms: selecting an edge-suitable backbone (ResNet-50) and employing metric learning (Triplet Loss) to improve convergence and generalization while adapting the stack for edge IoT deployment (Dockerized FastAPI with JWT). This clarifies that "optimization" in our title refers to model selection, loss design, and deployment efficiency on constrained devices. Additionally, the system's architectural principles are described, including the design of the web interface and server infrastructure. The proposed solution demonstrates the potential of intelligent biometric technologies in applications such as automated access control systems, educational institutions, smart buildings, and other areas where high reliability and resistance to spoofing are essential.