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PFPINet: An End-to-End Network for Partial Finger Photo Identification

Javad Khodadoust,Raúl Monroy,Emanuela Marasco,Ali Mohammad Khodadoust

Published 2026 in IEEE Transactions on Biometrics Behavior and Identity Science

ABSTRACT

Smartphones have become indispensable tools in daily life, serving various applications such as device unlocking, account login, online transactions, e-banking, e-health services, and access control. Recently, the use of smartphone cameras for fingerprint acquisition, known as finger photos, has gained attention as an alternative to dedicated fingerprint sensors due to its convenience, cost-effectiveness, and widespread availability. However, the quality of finger photos is significantly affected by various factors, including lighting conditions, camera resolution, finger or smartphone motion, out-of-focus capture, and the angle of finger placement. These factors can result in blurred or low-quality regions where ridge patterns cannot be reliably reconstructed. When certain regions are degraded, the remaining portion of the finger photo becomes partial, posing a significant challenge for recognition systems. In this paper, we propose PFPINet, a patch-based, end-to-end, single-to-multiple framework for partial finger photo identification. PFPINet comprises multiple modules, including three novel components introduced in this work: FreqQualNet, a deep convolutional neural network (DCNN) designed to assess the quality of finger photos; EnGAN, a generative adversarial network (GAN) developed to enhance image quality; and PaINet, a DCNN that incorporates a variant of k-means clustering for identification—unlike most existing approaches that focus solely on verification. Extensive experiments conducted on three finger photo datasets and two cross-database contactless fingerprint datasets demonstrate that PFPINet consistently outperforms current state-of-the-art (SOTA) methods.

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