Noncontact Palm Vein ROI Extraction Based on Improved Lightweight HRnet in Complex Backgrounds

Abstract

The extraction of ROI (region of interest) was a key step in noncontact palm vein recognition, which was crucial for the subsequent feature extraction and feature matching. A noncontact palm vein ROI extraction algorithm based on the improved HRnet for keypoints localization was proposed for dealing with hand gesture irregularities, translation, scaling, and rotation in complex backgrounds. To reduce the computation time and model size for ultimate deploying in low-cost embedded systems, this improved HRnet was designed to be lightweight by reconstructing the residual block structure and adopting depth-separable convolution, which greatly reduced the model size and improved the inference speed of network forward propagation. Next, the palm vein ROI localization and palm vein recognition are processed in self-built dataset and two public datasets (CASIA and TJU-PV). The proposed improved HRnet algorithm achieved 97.36% accuracy for keypoints detection on self-built palm vein dataset and 98.23% and 98.74% accuracy for keypoints detection on two public palm vein datasets (CASIA and TJU-PV), respectively. The model size was only 0.45 M, and on a CPU with a clock speed of 3 GHz, the average running time of ROI extraction for one image was 0.029 s. Based on the keypoints and corresponding ROI extraction, the equal error rate (EER) of palm vein recognition was 0.000362%, 0.014541%, and 0.005951% and the false nonmatch rate was 0.000001%, 11.034725%, and 4.613714% (false match rate: 0.01%) in the self-built dataset, TJU-PV, and CASIA, respectively. The experimental result showed that the proposed algorithm was feasible and effective and provided a reliable experimental basis for the research of palm vein recognition technology.