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Finger photo recognition represents a promising touchless technology that offers portable and hygienic authentication solutions in smartphones, eliminating physical contact. Public spaces, such as banks and staff-less stores, benefit from contactless authentication considering the current public health sphere. The user captures the image of their own finger by using the camera integrated in a mobile device. Although recent research has pushed boundaries of finger photo matching, the security of this biometric methodology still represents a concern. Existing systems have been proven to be vulnerable to print attacks by presenting a color paper-printout in front of the camera and photo attacks that consist of displaying the original image in front of the capturing device. This paper aims to improve the performance of finger photo presentation attack detection (PAD) algorithms by investigating deep fusion strategies to combine deep representations obtained from different color spaces. In this work, spoofness is described by combining different color models. The proposed framework integrates multiple convolutional neural networks (CNNs), each trained using patches extracted from a specific color model and centered around minutiae points. Experiments were carried out on a publicly available database of spoofed finger photos obtained from the IIITD Smartphone Finger photo Database with spoof data, including printouts and various display attacks. The results show that deep fusion of the best color models improved the robustness of the PAD system and competed with the state-of-the-art. 

Fingerprint-based authentication has been successfully adopted in a wide range of applications, including law enforcement and immigration, due to its numerous advantages over traditional password-based authentication. However, despite the usability and accuracy of this technology, some significant concerns still exist, which can potentially hinder its further adoption. For instance, a subject’s fingerprint is permanently associated with an individual and, once stolen, cannot be replaced, thus compromising biometric-based authentication. To mitigate this concern, we propose a multi-factor authentication approach that integrates type 1 and type 3 authentication factors into a fingerprint-based personal identification number, or FingerPIN. To authenticate, a subject is required to present a sequence of fingerprints corresponding to the digits of the PIN, based on a predefined secret mapping between digits and fingers. We conduct a vulnerability analysis of the proposed scheme, and demonstrate that it is robust to the compromise of one or more of the subject’s fingerprints.