Robust Face Morphing Attack Detection Using Fusion of Multiple Features and Classification Techniques

The face morphing process will combine two or more facial images to generate a single morphed facial image demonstrating Face Recognition Systems (FRS) vulnerability. The attack potential of the morphing image directly depends on the perceptual image quality, and when generated with no visible artefacts, it can deceive both human observers and automatic FRS. The current softwares for face morphing generates a morphing image with ghosting artefacts, especially in the eye region, nose and mouth area, which may serve as a potential cue to detect morphing attacks. Hence in this work, we introduce a new dataset comprising 10710 facial images before and after manual post-processing to reduce the visual artefacts and to generate high-quality attacks. Further, we propose a novel single image-based Morph Attack Detection (S-MAD) technique based on the ensemble of features and classifiers using the scale-space domain. The novel concept in the proposed method is the multilevel fusion that combines the comparison scores from different features and classifiers. Extensive experiments are carried out on the newly generated high-quality face images with (i) Morphs before post-processing and (ii) Morphs after post-processing. Further, the experiments are also carried out on two different mediums such as (i) Digital and (ii) Print-scan (or re-digitized) with and without compression. Extensive experimental results are performed to benchmark the detection performance with the existing S-MAD techniques. Obtained results indicate the best performance of the proposed method over existing methods.