Detecting Morphed Face Images Using Facial Landmarks

With the widespread deployment of automatic biometric recognition systems, some security issues have been unveiled. In particular, face recognition systems have been recently shown to be vulnerable to attacks carried out with morphed face images. Such synthetic images can be defined as the fusion of the face images of two (or more) different subjects. The associated risk lies on the ability of multiple subjects to be positively verified with a single enrolled morphed face image. As common texture based features have limited capabilities to tackle this problem, we propose a novel method for morphed face image detection, based on the computation of the differences between the landmarks of a probe bona fide (i.e., captured under supervision) image of the attacker, and the landmarks of the enrolled image (i.e., the suspected morphed image). In this work, a new database is created for the experiments, comprising both bona fide and morphed images created with two different morphing methods. The experiments show that for the detection task, the proposed algorithm achieves Equal Error Rates at 32.7%.