Monocular 3D Fingerprint Reconstruction and Unwarping

Compared with contact-based fingerprint acquisition techniques, contactless acquisition has the advantages of less skin distortion, more complete fingerprint area, and hygienic acquisition. However, perspective distortion is a challenge in contactless fingerprint recognition, which changes the ridge frequency and relative minutiae location, and thus degrades the recognition accuracy. We propose a learning-based shape-from-texture algorithm to reconstruct a 3-D finger shape from a single image and unwarp the raw image to suppress the perspective distortion. Our experimental results for 3-D reconstruction on contactless fingerprint databases show that the proposed method has high 3-D reconstruction accuracy. Experimental results for contactless-to-contactless and contactless-to-contact-based fingerprint matching indicate that the proposed method can improve the matching accuracy.

Automated summary

Compared with contact-based fingerprint acquisition techniques, contactless acquisition has the advantages of less skin distortion, more complete fingerprint area, and hygienic acquisition. However, perspective distortion is a challenge in contactless fingerprint recognition, which changes the ridge frequency and relative minutiae location, and thus degrades the recognition accuracy. We propose a learning-based shape-from-texture algorithm to reconstruct a 3-D finger shape from a single image and unwarp the raw image to suppress the perspective distortion. Our experimental results show that the proposed method has high 3-D reconstruction accuracy and improves matching accuracy for contactless fingerprint recognition.