Multispectral Deep Embeddings as a Countermeasure to Custom Silicone Mask Presentation Attacks

Abstract

This work focuses on detecting presentation attacks (PA) mounted using custom silicone masks. Face recognition (FR) systems have been shown to be highly vulnerable to PAs based on such masks. Here we explore the use of multispectral data (color imagery, near infrared (NIR) imagery and thermal imagery) for face presentation attack detection (PAD), specifically against the custom silicone mask attacks. Using a new dataset (XCSMAD) representing 21 custom made masks, we establish the baseline performance of several commonly used face-PAD methods, on the different imaging channels. Considering thermal imagery in particular, our experiments show that low-cost thermal imaging devices are as effective in face-PAD as more expensive thermal cameras, for mask-based attacks. This result reinforces the case for the use of thermal data in face-PAD. We also demonstrate that fusing information from multiple channels leads to significant improvement in face-PAD performance. Finally, we propose a new approach to face-PAD of custom silicone masks using a convolutional neural network (CNN). On individual spectral channels, the proposed approach achieves state-of-the-art results. Using multispectral-fusion, the proposed CNN-based method significantly outperforms the baseline methods. The new dataset and source-code for our experiments is freely available for research purposes.