CNN-Based Anomaly Detection For Face Presentation Attack Detection With Multi-Channel Images

Abstract

Recently, face recognition systems have received significant attention, and there have been many works focused on presentation attacks (PAs). However, the generalization capacity of PAs is still challenging in real scenarios, as the attack samples in the training database may not cover all possible PAs. In this paper, we propose to perform the face presentation attack detection (PAD) with multi-channel images using the convolutional neural network based anomaly detection. Multi-channel images endow us with rich information to distinguish between different mode of attacks, and the anomaly detection based technique ensures the generalization performance. We evaluate the performance of our methods using the wide multi-channel presentation attack (WMCA) dataset.