Face Feature Visualisation of Single Morphing Attack Detection
This work addresses the need for interpretable tools in border security to help personnel detect morphing attacks, though it is incremental as it builds on existing feature extraction methods.
The paper tackled the problem of detecting morphed face images by proposing an explainable visualization of face feature extraction algorithms, achieving the best results with Discrete Cosine-Transformation for synthetic images and BSIF for landmark-based features.
This paper proposes an explainable visualisation of different face feature extraction algorithms that enable the detection of bona fide and morphing images for single morphing attack detection. The feature extraction is based on raw image, shape, texture, frequency and compression. This visualisation may help to develop a Graphical User Interface for border policies and specifically for border guard personnel that have to investigate details of suspect images. A Random forest classifier was trained in a leave-one-out protocol on three landmarks-based face morphing methods and a StyleGAN-based morphing method for which morphed images are available in the FRLL database. For morphing attack detection, the Discrete Cosine-Transformation-based method obtained the best results for synthetic images and BSIF for landmark-based image features.