Style Transfer Applied to Face Liveness Detection with User-Centered Models
This addresses face liveness detection for security applications, but it is incremental as it builds on existing style transfer and CNN methods.
The paper tackled the problem of obtaining fraudulent images for training face anti-spoofing models by using style transfer to generate spoof images, resulting in an average classification error rate of 0.22 on the SiW database.
This paper proposes a face anti-spoofing user-centered model (FAS-UCM). The major difficulty, in this case, is obtaining fraudulent images from all users to train the models. To overcome this problem, the proposed method is divided in three main parts: generation of new spoof images, based on style transfer and spoof image representation models; training of a Convolutional Neural Network (CNN) for liveness detection; evaluation of the live and spoof testing images for each subject. The generalization of the CNN to perform style transfer has shown promising qualitative results. Preliminary results have shown that the proposed method is capable of distinguishing between live and spoof images on the SiW database, with an average classification error rate of 0.22.