Deep learning based face liveness detection in videos

The human face is an important biometric quantity which can be used to access a user-based system. As human face images can easily be obtained via mobile cameras and social networks, user-based access systems should be robust against spoof face attacks. In other words, a reliable face-based access system can determine both the identity and the liveness of the input face. To this end, various feature-based spoof face detection methods have been proposed. These methods generally apply a series of processes against the input image(s) in order to detect the liveness of the face. In this paper, a deep-learning-based spoof face detection is proposed. Two different deep learning models are used to achieve this, namely local receptive fields (LRF)-ELM and CNN. LRF-ELM is a recently developed model which contains a convolution and a pooling layer before a fully connected layer that makes the model fast. CNN, however, contains a series of convolution and pooling layers. In addition, the CNN model may have more fully connected layers. A series of experiments were conducted on two popular spoof face detection databases, namely NUAA and CASIA. The obtained results were then compared, and the LRF-ELM method yielded better results against both databases.