Finger photo recognition has emerged as an alternative biometric authentication solution in smartphones, leveraging common RGB cameras to acquire images of human fingers, improving hygiene and user experience. The security of this technology is currently threatened by presentation attacks. Although being equipped with presentation attack detection modules is of critical importance for these systems, existing approaches are not robust to several challenges including unknown attacks and device diversity. The limited availability of training data to the research community has constrained progress. In this paper, we present two new databases of finger photos for developing anti-spoofing countermeasures, Mason Finger Photo Presentation Attack Detection iPhone 13 Pro 2022 (MFPAD-i-22) and Mason Finger Photo Presentation Attack Detection Google Pixel 32023 (MFPAD-G-23), containing live and spoof finger photos with associated demographics. MFPAD-i-22 was acquired from 112 subjects using the device iPhone 13 Pro, while MFPAD-G-23 from 100 individuals using Google Pixel 3. We also discuss a novel mobile App we developed in an Android environment based on a previously designed PAD fusing different color spaces. By providing these resources and insights, we encourage researchers to spend efforts to advance contactless fingerprint PAD in mobiles, for more secure and robust biometric systems.

A fingerphoto is obtained by imaging a human finger using a basic smartphone camera. Although impressive advances have been made to accurately match fingerphotos, this technology is vulnerable to presentation attacks (PAs). These algorithms do not generalize well in the presence of new presentation attacks. While previous research on this issue is limited, this paper systematically evaluates fingerphoto presentation attack detection (PAD) algorithms under unknown attacks. The proposed assessment compares different Convolutional Neural Networks (CNNs) on the IIITD Smartphone Fingerphoto database with spoof data including printout and various display attacks. These images used for the experiments were acquired indoors and subjected to background (i.e., white or natural) and capture device (i.e., Nokia or OPO) variations. Preliminary results show that the PAD based on AlexNet is robust under most types of replica unseen during the training of the detector.