基于边缘计算的紧致化掌纹识别系统

With the development of biometric authentication technology, contactless palmprint recognition has gained increasing attention due to its high recognition rate, low invasiveness, and contact lessness. To meet the demand of real-time and efficient processing of multiple devices in the edge environment, this paper designs a compact palmprint recognition framework based on edge computing and builds a palmprint recognition subsystem in the terminal device, edge server, and cloud layers. In the terminal device layer, the Tiny YOLO-v3-based target recognition algorithm and the MobileNetV2-based keypoint localization algorithm are used to pre-process the captured images, extract the palmprint region of interest (ROI), and make recognition requests. At the edge server layer, the received palmprint ROIs are extracted and matched with features using the GoogLeNet model based on adversarial metric learning. The data are then synchronized after the recognition results are returned. In the data center, all recognition tasks are logged and filed in the database. Moreover, the network models of the end and edge devices are regularly trained and updated to improve the system's crossdomain recognition capability. The constructed framework is a complete and feasible biometric recognition framework with broad market prospects and application value.