Differential Morphing Attack Detection via Triplet-Based Metric Learning and Artifact Extraction

Face morphing attack has been demonstrated to pose significant security risks to face recognition systems. Therefore, recently, developing reliable Morphing Attack Detection (MAD) techniques has become an important research priority. This paper considers the failure of regular face recognition systems in handling morphed faces from the perspective of identity feature space, arguing that the morphed image creates a 'pathway' between the two subjects involved in the morphing generation process. This 'pathway' causes automatic face recognition systems to misidentify the morphed face as either contributing subjects. Based on this insight and considering the characteristics of the Differential Morphing Attack Detection (D-MAD) scenario (i.e., both the potential morphing attack image and the trusted live face image are accessible), we propose an end-to-end D-MAD solution based on metric learning with triplet feature separation and artifact analysis. The proposed approach aims to break the 'pathway' created by the morphed image in the identity feature space and to incorporate latent artifact features of the potential morphed image for D-MAD. Comparative results on different public benchmarks indicate that the proposed solution demonstrates satisfactory performance against state-of-the-art algorithms.