Improved deep learning-based contactless biometric recognition using bracelet lines

This study explores the integration of deep learning and computer vision techniques for contactless biometric identification, specifically focusing on wrist bracelet lines. In an era where biometric identification plays a pivotal role in various sectors, including personal identity, mobile devices, and smart gadgets, this research addresses the heightened demand for efficient contactless solutions, amplified by the COVID-19 pandemic. While conventional methods such as finger knuckle, face recognition, and fingerprint analysis have been prevalent, this research introduces an innovative approach that capitalizes on the distinct and enduring patterns observed in wrist bracelet lines. The proposed system employs advanced deep learning methodologies, notably the YOLO (You Only Look Once) model for precise wrist detection. To facilitate image capture and transmission for identification, a combination of an Arduino Uno and an ESP32 CAM module is employed. The study highlights the system's real-time recognition capabilities through comprehensive results, emphasizing its potential for secure, efficient, and contactless biometric identification in Smart city applications.