Distinctive Feature Representation for Contactless 3D Hand Biometrics using Surface Normal Directions

Contactless 3D hand biometrics offers hygienic and convenient approaches for biometric recognition. This paper investigates a distinctive feature representation using 3D surface normal information for more accurate 3D hand biometric identification. Prior research on contactless 3D hand biometric identification largely incorporates 3D depth and surface curvature information to recover discriminative features. Our investigation presented in this paper indicates that extracting distinctive features from surface normal information, which can also be directly obtained from low-cost photometric stereo based imaging systems, can offer a computationally simpler alternative and is therefore highly desirable. The directions of neighbouring surface normal vectors can encode frequently observed irregular ridge and valley regions, which can enable more accurate surface feature description. Comparative experimental results presented in this paper validates the effectiveness of the proposed approach.