Dr. Sudipta Banerjee and her student Prateek Jaiswal, MS by Research, presented a paper as a poster on Facial De-Morphing: Extracting Component Faces from a Single Morph at the International Joint Conference in Biometrics (IJCB-2022), Abu Dhabi, UAE. The other author of this research work is Prof. Arun Ross, Department of Computer Science and Engineering at Michigan State University.
Research work as explained by the authors: A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as de-morphing. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice – i.e., the other identity. In this work, we propose a novel demorphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.
Conference page: https://ijcb2022.org/#/
Technical program: https://ijcb2022.org/#/programs