Exposing Presentation Attacks by a Combination of Multi-intrinsic Image Properties, Convolutional Networks and Transfer Learning

Abstract

Nowadays, adoption of face recognition for biometric authentication systems is widespread, mainly because this is one of the most accessible biometric characteristic. Techniques intended on deceive these kinds of systems by using a forged biometric sample, such as a printed paper or a recorded video of a genuine access, are known as presentation attacks. Presentation Attack Detection is a crucial step for preventing this kind of unauthorized accesses into restricted areas or devices. In this paper, we propose a new method that relies on a combination of the intrinsic properties of the image with deep neural networks to detect presentation attack attempts. Exploring depth, salience and illumination properties, along with a Convolutional Neural Network, proposed method produce robust and discriminant features which are then classified to detect presentation attacks attempts. In a very challenging cross-dataset scenario, proposed method outperform state-of-the-art methods in two of three evaluated datasets.