Face anti-spoofing via deep local binary patterns

Convolutional neural networks (CNNs) have achieved excellent performance in the field of pattern recognition when huge amount of training data is available. However, training a CNN model is less obvious when only a limited amount of data is given such as in the case of face anti-spoofing problem. It is indeed not easy to collect very large sets of fake faces. Especially for the fully-connected layers, tens of thousands of parameters need to be learned. To tackle this problem of lack of training data in face anti-spoofing, we propose to explore the incorporation of hand-crafted features in the CNN framework. In our proposed approach, the color local binary patterns (LBP) features are extracted from the convolutional feature maps, which are fine tuned based on the VGG-face model. These features are then fed into support vector machine (SVM) classifier. Extensive experiments are conducted on two benchmark and publicly available databases showing very interesting performance compared to state-of-the-art methods.