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Contactless biometric hand geometry recognition using a low-cost 3D camera

In the past decade, the interest in using 3D data for biometric person authentication has increased significantly, propelled by the availability of affordable 3D sensors. The adoption of 3D features has been especially successful in face recognition applications, leading to several commercial 3D face recognition products. In other biometric modalities such as hand recognition, several studies have shown the potential advantage of using 3D geometric information, however, no commercial-grade systems are currently available. In this paper, we present a contactless 3D hand recognition system based on the novel Intel RealSense camera, the first mass-produced embeddable 3D sensor. The small form factor and low cost make this sensor especially appealing for commercial biometric applications, however, they come at the price of lower resolution compared to more expensive 3D scanners used in previous research. We analyze the robustness of several existing 2D and 3D features that can be extracted from the images captured by the RealSense camera and study the use of metric learning for their fusion.