The detection of spoofing by 3D mask in a 2D identity recognition system

Nowadays face recognition systems are facing a new problem after having won the challenge of reliability. The problem is that these systems have become vulnerable to attacks by identity theft. In order to deceive the recognition systems hackers use several methods, such as the use of face images or videos of people belonging to the system database. Luckily, this type of attack is thwarted by the use of adapted systems. But unfortunately another type of attack that uses 3D face masks appeared. This type of attack is very efficient, since as will be shown, a high percentage of hackers who use 3D masks can mislead a good facial recognition system, like the one used in our investigation. In this paper, a new method is proposed for the detection of hackers that use 3D masks to deceive face recognition systems. This method uses the Angular Radial Transformation (ART) to extract pertinent features that are fed into a classifier to decide whether the captured image represents a face image. The performance of the proposed method was evaluated using a public 3D Mask Attack Database (3DMAD). The obtained results show the efficiency of the proposed method, since it can reduce the error rate in discriminating between a real face and a face mask down to 0.90%.