  Face recognition is widely employed in Automated Border Control (ABC) gates,
which verify the face image on passport or electronic Machine Readable Travel
Document (eMTRD) against the captured image to confirm the identity of the
passport holder. In this paper, we present a robust morph detection algorithm
that is based on differential morph detection. The proposed method decomposes
the bona fide image captured from the ABC gate and the digital face image
extracted from the eMRTD into the diffuse reconstructed image and a quantized
normal map. The extracted features are further used to learn a linear
classifier (SVM) to detect a morphing attack based on the assessment of
differences between the bona fide image from the ABC gate and the digital face
image extracted from the passport. Owing to the availability of multiple
cameras within an ABC gate, we extend the proposed method to fuse the
classification scores to generate the final decision on morph-attack-detection.
To validate our proposed algorithm, we create a morph attack database with
overall 588 images, where bona fide are captured in an indoor lighting
environment with a Canon DSLR Camera with one sample per subject and
correspondingly images from ABC gates. We benchmark our proposed method with
the existing state-of-the-art and can state that the new approach significantly
outperforms previous approaches in the ABC gate scenario.
