Single Image Face Morphing Attack Detection Using Ensemble of Features

Abstract

Face morphing attacks have demonstrated a severe threat in the passport issuance protocol that weakens the border control operations. A morphed face images if used after printing and scanning (re-digitizing) to obtain a passport is very challenging to be detected as attack. In this paper, we present a novel method to detect such morphing attacks using an ensemble of features computed on the scale-space representation derived from the color space for a given image. Given the limited availability of datasets representing realistic morphing attacks, we introduce and present a new print-scan image dataset of morphed face images. Experiments are carried out on the two different datasets and compared with sixteen existing state-of-art Morphing Attack Detection (MAD) mechanism based on single image MAD (S-MAD). The proposed approach indicates a superior MAD performance on both datasets suggesting the applicability in operational scenarios.