Anomaly Detection-Based Unknown Face Presentation Attack Detection

TL;DR

This paper introduces a deep learning-based anomaly detection method for face presentation attack detection that learns classifier and features end-to-end, using pseudo-negative classes and pairwise confusion loss, demonstrating improved results on multiple datasets.

Contribution

It proposes a novel end-to-end deep learning approach with pseudo-negative classes and pairwise confusion loss for face PAD, enhancing generalization to unseen attacks.

Findings

Outperforms previous methods on four datasets

Effective in detecting unknown spoof attacks

Improves feature learning for face PAD

Abstract

Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users. These detectors are of practical importance as they are shown to generalize well to new attack types. In this paper, we present a deep-learning solution for anomaly detection-based spoof attack detection where both classifier and feature representations are learned together end-to-end. First, we introduce a pseudo-negative class during training in the absence of attacked images. The pseudo-negative class is modeled using a Gaussian distribution whose mean is calculated by a weighted running mean. Secondly, we use pairwise confusion loss to further regularize the training process. The proposed approach benefits from the representation learning power of the CNNs and learns better features for fPAD task as shown in our ablation study. We perform extensive experiments on four publicly available datasets: Replay-Attack, Rose-Youtu, OULU-NPU and Spoof in Wild to show the effectiveness of the proposed approach over the previous methods. Code is available at: \url{https://github.com/yashasvi97/IJCB2020_anomaly}