FaceSpoof Buster: a Presentation Attack Detector Based on Intrinsic Image Properties and Deep Learning
This addresses security vulnerabilities in biometric authentication systems, preventing unauthorized access, but appears incremental as it builds on existing methods with a novel combination.
The paper tackles the problem of presentation attack detection in face recognition systems by combining intrinsic image properties (depth, salience, illumination) with deep learning, achieving state-of-the-art results in inter-dataset protocols.
Nowadays, the adoption of face recognition for biometric authentication systems is usual, mainly because this is one of the most accessible biometric modalities. Techniques that rely on trespassing these kind 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, but may be also referred in the literature as face spoofing. Presentation attack detection is a crucial step for preventing this kind of unauthorized accesses into restricted areas and/or devices. In this paper, we propose a novel approach which relies in a combination between intrinsic image properties and deep neural networks to detect presentation attack attempts. Our method explores depth, salience and illumination maps, associated with a pre-trained Convolutional Neural Network in order to produce robust and discriminant features. Each one of these properties are individually classified and, in the end of the process, they are combined by a meta learning classifier, which achieves outstanding results on the most popular datasets for PAD. Results show that proposed method is able to overpass state-of-the-art results in an inter-dataset protocol, which is defined as the most challenging in the literature.