CNN PATCH POOLING FOR DETECTING 3D MASK PRESENTATION ATTACKS IN NIR

Presentation attacks using 3D masks pose a serious threat to face recognition systems. Automatic detection of these attacks is challenging due to hyper-realistic nature of masks. In this work, we consider presentations acquired in near infrared (NIR) imaging channel for detection of mask-based attacks. We propose a patch pooling mechanism to learn complex textural features from lower layers of a convolutional neural network (CNN). The proposed patch pooling layer can be used in conjunction with a pretrained face recognition CNN without fine-tuning or adaptation. The pretrained CNN, in fact, can also be trained from visual spectrum data. We demonstrate efficacy of the proposed method on mask attacks in NIR channel from WMCA and MLFP datasets. It achieves near perfect results on WMCA data, and outperforms existing benchmark on MLFP dataset by a large margin.