Towards face anti-spoofing

Face anti-spoofing is an important part of the face recognition algorithm that aims to prevent face presentation attacks. To facilitate face anti-spoofing research, this paper introduces a novel method in which a simple model provides enhanced performance on the RGB modality images of the CASIA-SURF dataset. Initially, a simple model based on ResNet-50 architecture is proposed that uses three residual layers of ResNet-50 along with a single neuron at the end for binary classification. Meanwhile, to judge the baseline performance, the proposed Resnet-50-based model is trained on the RGB and Depth images, separately. Later, to increase the performance of the proposed ResNet-50-based model on the RGB modality images, a novel Learning Using Privileged Information (LUPI) algorithm is applied. The LUPI contains Depth images and helps to train the inferior model with the help of a pre-trained superior model. Detailed simulations on the CASIA-SURF dataset reveal that an intelligent combination of the ResNet50 based model with the LUPI paradigm works exceptionally well and significantly improves the performance on the RGB modality of the images. The proposed model is less complex and can be deployed in resource-constrained environments.