MixNet for Generalized Face Presentation Attack Detection

TL;DR

This paper introduces MixNet, a deep learning model designed to improve face presentation attack detection, especially its ability to generalize across different attack types and unseen databases, outperforming existing methods.

Contribution

The paper presents MixNet, a novel deep learning architecture that enhances generalization in face presentation attack detection across multiple attack types and unseen datasets.

Findings

MixNet outperforms existing algorithms on challenging databases.

It demonstrates strong generalization to unseen attack types.

Extensive experiments validate its effectiveness.

Abstract

The non-intrusive nature and high accuracy of face recognition algorithms have led to their successful deployment across multiple applications ranging from border access to mobile unlocking and digital payments. However, their vulnerability against sophisticated and cost-effective presentation attack mediums raises essential questions regarding its reliability. In the literature, several presentation attack detection algorithms are presented; however, they are still far behind from reality. The major problem with existing work is the generalizability against multiple attacks both in the seen and unseen setting. The algorithms which are useful for one kind of attack (such as print) perform unsatisfactorily for another type of attack (such as silicone masks). In this research, we have proposed a deep learning-based network termed as \textit{MixNet} to detect presentation attacks in cross-database and unseen attack settings. The proposed algorithm utilizes state-of-the-art convolutional neural network architectures and learns the feature mapping for each attack category. Experiments are performed using multiple challenging face presentation attack databases such as SMAD and Spoof In the Wild (SiW-M) databases. Extensive experiments and comparison with existing state of the art algorithms show the effectiveness of the proposed algorithm.