A Novel Multicamera System for High-Speed Touchless Palm Recognition

Palm-related biometrics have been widely studied for a long time, as the palm contains many distinctive patterns. However, most of the existing systems are designed to work within an ideal environment, such as in front of a unicolor background or in a large enclosure. Those preconditions can avoid influences of ambient light and hand distance change, but at the same time, they also limit the applications of palm recognition. In the work reported in this paper, we designed a novel red-green-blue and depth-based four-camera system that can capture the palm-related images separately in real time. The techniques of region-of-interest (ROI) location, ROI alignment, and light-source intensity optimization were studied. The ROI location method is modified to increase the robustness of hand gesture variation. Based on the depth information, we proposed the coordinate mapping and inclination rectification methods to obtain aligned ROI pairs. Using this device, we collected a video-based multimodal palm image database. After the parameter optimization and information fusion, the equal-error-rate of our approach on this database is lower than 0.47%. The recognition rate obtained from the support-vector-machine-based fusion is higher than 99.8%. The experimental results prove that the proposed system achieves advantages of anti-spoofing, high speed, high accuracy, and small size.