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Dense 3D Face Correspondence for Deep 3D Face Recognition and Medical Applications

Dense 3D Face Correspondence for Deep 3D Face Recognition and Medical Applications

Speaker: Ajmal Saeed Mian – Crawley, WN, Australia
Topic(s): Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing

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 correspond-ence 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, accurate dense correspond-ences are iteratively propagated between hundreds of faces without using a prior model. Thus, our dense correspondences are not biased by a prior model allowing us 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, ex-pressions 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. Finally, I will show results of our 3D facial analysis to medial applications in the diagnosis of sleep apnoea and autism, as well as reconstructive craniofacial surgery plan-ning and outcome analysis.

About this Lecture

Number of Slides: 53
Duration: 50 minutes
Languages Available: English
Last Updated:

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