Towards making Morphing Attack Detection robust using hybrid Scale-Space Colour Texture Features

The widespread use of face recognition algorithms, especially in Automatic Border Control (ABC) systems has raised concerns due to potential attacks. Face morphing combines more than one face images to generate a single image that can be used in the passport enrolment procedure. Such morphed passports have proven to be a significant threat to national security, as two or more individuals that contributed to the morphed reference image can use that single travel document. In this work, we present a novel method based on hybrid colour features to automatically detect morphed face images. The proposed method is based on exploring multiple colour spaces and scale-spaces using a Laplacian pyramid to extract robust features. The texture features corresponding to each scale-space in different color spaces are extracted with Local Binary Patterns (LBP) and classified using a Spectral Regression Kernel Discriminant Analysis (SRKDA) classifier. The scores are further fused using sum rule to detect the morphed face images. Experiments are carried out on a large-scale morphed face image database consisting of printed and scanned images to reflect the real-life passport issuance scenario. The evaluation database consists of images comprised of 1270 bona fide face images and 2515 morphed face images. The performance of the proposed method is compared with seven different deep learning and seven different non-deep learning methods, which has indicated the best performance of the proposed scheme with Bona fide Presentation Classification Error (BPCER) = 0.86% @ Attack Presentation Classification Error Rate (APCER) = 10% and BPCER = 7.59% @ APCER = 5%. The obtained results indicate the robustness in detecting morphing attacks as compared to earlier works.