A self-immune to 3D masks attacks face recognition system

Abstract

Due to their advantages, facial recognition systems are among the most widely used biometric systems in recent years. However, their drawback is that they can easily be deceived by using 3D masks, which are replicas of real faces. To confirm this fact, we have tested the vulnerability to 3D masks attacks of the already approved Legendre moments invariants (LMI)-based face recognition method. This has been achieved by using the 3D mask attack database (3DMAD), which consists of real faces and faces with 3D masks. The obtained spoof false acceptance rate (SFAR) was close to 65%, which proves that this recognition system is vulnerable to 3D masks attacks. This is generally the case of other face recognition systems, with no anti-spoofing provision. In this paper, a face recognition method is proposed to prevent hackers from deceiving face recognition systems by using 3D masks of people belonging to the system database. This method combines the LMI and the linear discriminant analysis for characteristic features extraction, and the maximum likelihood for classification. With a 97.6% recognition rate and a SFAR equal to 0.83%, the results obtained may be considered as very satisfactory. These results, while obtained with a lower computational time, compare favorably with those of the state-of-the-art method that uses the same 3DMAD database.