Advances in Visual Computing: 14th International Symposium on Visual Computing, ISVC 2019, Lake Tahoe, NV, USA, October 7–9, 2019, Proceedings, Part I

Abstract

In this talk, I will present our research on dense 3D face correspondence which is a core problem in facial analysis for many applications such as biometric identification, symptomatology for the diagnosis of Autism and Obstructive Sleep Apnoea and planning for facial reconstructive surgery. From a morphometric point of view, we are interested in performing dense correspondence based purely on shape without using texture. This makes the problem challenging but the correspondences and subsequent analyses more precise. The idea is to start from a sparse set of automatically detected corresponding landmarks and propagate along the geodesics connecting nearby points. By anchoring on the most reliable correspondences for propagation, accurate dense correspondences are iteratively established between hundreds of faces without using a prior model. Thus, we are able to construct population specific deformable face models for symptomatology and patient specific morphs to facial norms for reconstructive surgery. Moreover, by establishing dense correspondences between different facial identities and expressions, we synthesize millions of 3D faces with varying identities, expressions and poses to learn a deep Convolutional Neural Network (FR3DNet) for large scale 3D face recognition. FR3DNet achieves state-of-the-art results, outperforming existing methods in open-world and close-world face recognition, on a dataset four times the largest dataset reported in the existing literature. Approaches to Massive Scientific Data Visualization and Analysis