Recognizing Palm Vein in Smartphones Using RGB Images

Abstract

Recently, demand for biometric access controls and online payments in smartphones increased, necessitating further investigation and development in this area. This article proposes a new low-cost palm vein recognition system for smartphones using red, green, blue (RGB) images. First, we detect and enhance palm vein patterns, using the saturation channel instead of the red channel as in the existing approaches. Then, to address the challenging contactless capturing problems of smartphones—such as scale variants, rotation, closed fingers, or rings on hand—we introduce an improved method for the region of interest extraction, based on the convex hull, with a new idea for key vector use. We also designed a new lightweight deep learning-based model for smartphones, which was overlooked in previous palm vein recognition studies. The proposed model comprises suitable blocks of convolution, depthwise separable convolution, inverted residual bottleneck, and spatial pyramid pooling module; in addition, the accuracy is enhanced with fusion strategy. Results show that the proposed model is both smaller and more accurate than related models. The integrated proposed model obtains the best equal error rate, 0.49%, and an inference time of 8 ms.