Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Lane Department of Computer Science and Electrical Engineering

Committee Chair

Nasser Nasrabadi

Committee Member

Gianfranco Doretto

Committee Member

Jeremy Dawson

Committee Member

Omid Dehzangi


Contactless fingerprint matching is a common form of biometric security today. Most smartphones and associated apps now let users opt into using this form of biometric security. However, it’s difficult to match a finger-photo to a fingerprint because of perspective distortion occurring at the edges of the finger-photo, so direct matching using conventional methods will not be as accurate due to a lack of sufficient matching minutiae points. To address this issue, we propose a deep model, Perspective Distortion Rectification Model (PDRM), to estimate the fingerprint correspondence for finger-photo images in order to recover more minutiae points. Not only do we determine the feasibility of matching synthesized fingerprints from finger-photos, but we also show that matching a finger-photo to a fingerprint directly is possible by using our proposed Coupled Generative Adversarial Network (CpGAN) verifier. The results from our PDRM show that our method for creating synthetic fingerprints from finger-photos provides a more accurate matching (AUC=96.4%, EER= 8.9%) than just using the same commercial matcher to match finger-photo and fingerprints directly (AUC=92.1%, EER=15.7%). Finally, our proposed CpGAN verifier provides the best matching accuracy with AUC=98.4% and EER=6.3%.