A Novel Photometric Method for Real-Time 3D Reconstruction of Fingerprint

3D fingerprint recognition is an emerging technology in biometrics. However, current 3D fingerprint acquisition systems are usually with complex structure and high-cost and that has become the main obstacle for its popularization. In this work, we present a novel photometric method and an experimental setup for real-time 3D fingerprint reconstruction. The proposed system consists of seven LED lights that mounted around one camera. In the surface reflectance modeling of finger surface, a simplified Hanrahan-Krueger model is introduced. And a neural network approach is used to solve the model for accurate estimation of surface normals. A calibration method is also proposed to determine the lighting directions as well as the correction of the lighting fields. Moreover, to stand out the fingerprint ridge features and get better visual effects, a linear transformation is applied to the recovered normals field. Experiments on live fingerprint and the comparison with traditional photometric stereo algorithm are used to demonstrate its high performance.