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,

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... tive 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%.