Enhancing Mobile Privacy and Security: A Face Skin Patch-Based Anti-Spoofing Approach

TL;DR

This paper introduces a privacy-preserving face anti-spoofing method using facial skin patches, which improves security, speed, and accuracy in facial recognition systems by eliminating privacy risks associated with image transmission.

Contribution

The novel approach uses facial skin patches as input for anti-spoofing, avoiding privacy issues and enhancing speed and accuracy over traditional methods.

Findings

Demonstrated superior accuracy on public datasets.

Achieved faster inference times compared to traditional methods.

Effectively prevents privacy leakage during anti-spoofing.

Abstract

As Facial Recognition System(FRS) is widely applied in areas such as access control and mobile payments due to its convenience and high accuracy. The security of facial recognition is also highly regarded. The Face anti-spoofing system(FAS) for face recognition is an important component used to enhance the security of face recognition systems. Traditional FAS used images containing identity information to detect spoofing traces, however there is a risk of privacy leakage during the transmission and storage of these images. Besides, the encryption and decryption of these privacy-sensitive data takes too long compared to inference time by FAS model. To address the above issues, we propose a face anti-spoofing algorithm based on facial skin patches leveraging pure facial skin patch images as input, which contain no privacy information, no encryption or decryption is needed for these images. We conduct experiments on several public datasets, the results prove that our algorithm has demonstrated superiority in both accuracy and speed.