Face Morphing of Newborns Can Be Threatening Too : Preliminary Study on Vulnerability and Detection

Abstract

Face morphing attacks are evolving as a significant threat to the Face Recognition Systems (FRS) operating in border control and passport issuance. As newborn face has very limited discriminative facial characteristics, it is challenging for both human and machines to verify the newborns based on the facial biometrics accurately. Further, the introduction of face morphing elevates the problem of baby trafficking as it can challenge both human and machine-based facial verification. In this paper, we pose a question if the morphed images of newborns can threaten FRS and present first systematic study on the vulnerability analysis of FRS towards morphed faces of newborns. To effectively benchmark threat of newborns’ facial morphing attacks, we introduce a new face morphing dataset constructed based on 42 unique newborns with 852 bona fide and 2451 morphing images. Extensive experiments are carried out on the newly constructed dataset to benchmark the vulnerability against both Commercial-Off-The-Shelf (COTS) FRS (Cognitec FaceVACS-SDK Version 9.4.2) and deep learning based FRS (Arcface) for three different morphing factors. Further, we also evaluate the performance of Morphing Attack Detection (MAD) in detecting such morphing attacks of newborn faces. We conduct experiments on four different Off-The-Shelf MAD techniques to benchmark the detection performance on newborn morph attacks.