Detection of presentation attacks on facial authentication systems using special devices

Abstract

The article proposes a feature system designed to detect presentation attacks on facial authentication systems. In this type of attack, an attacker disguises as an authorized user using his image. The feature system assumes the possibility of using one or more special imaging sensors in addition to the basic RGB camera (thermal cameras, depth cameras, infrared cameras). The method has demonstrated a low error rate on the WMCA dataset, while experiments have shown its ability to remain effective in the case of the lack of training data. The comparative experiments carried out showed that the proposed method surpassed the RDWT-Haralick-SVM algorithm, and also approached the results of the MC-CNN algorithm, based on deep learning, which requires a significantly larger amount of training data.