Single Morphing Attack Detection using Feature Selection and Visualisation based on Mutual Information
This work addresses the problem of detecting morphed faces in passport verification systems, which is an incremental improvement in domain-specific security applications.
The paper tackled face morphing attack detection by proposing a feature selection method based on Mutual Information to identify critical facial areas, achieving results using only 500 features for FERET and 800 for FRGCv2 images out of 1,048 available.
Face morphing attack detection is a challenging task. Automatic classification methods and manual inspection are realised in automatic border control gates to detect morphing attacks. Understanding how a machine learning system can detect morphed faces and the most relevant facial areas is crucial. Those relevant areas contain texture signals that allow us to separate the bona fide and the morph images. Also, it helps in the manual examination to detect a passport generated with morphed images. This paper explores features extracted from intensity, shape, texture, and proposes a feature selection stage based on the Mutual Information filter to select the most relevant and less redundant features. This selection allows us to reduce the workload and know the exact localisation of such areas to understand the morphing impact and create a robust classifier. The best results were obtained for the method based on Conditional Mutual Information and Shape features using only 500 features for FERET images and 800 features for FRGCv2 images from 1,048 features available. The eyes and nose are identified as the most critical areas to be analysed.