Deep, dense and accurate 3D face correspondence for generating population specific deformable models

Syed Zulqarnain Gilani; Ajmal Mian; Peter Eastwood

doi:10.1016/j.patcog.2017.04.013

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Deep, dense and accurate 3D face correspondence for generating population specific deformable models

Journal article

Peer reviewed

Deep, dense and accurate 3D face correspondence for generating population specific deformable models

Syed Zulqarnain Gilani, Ajmal Mian and Peter Eastwood

Pattern recognition, Vol.69, pp.238-250

2017

DOI: https://doi.org/10.1016/j.patcog.2017.04.013

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Abstract

Dense 3D face correspondence

3D face morphing

Keypoint detection

Shape descriptor

Face recognition

Landmark identification

Deep learning

We present a multilinear algorithm to automatically establish dense point-to-point correspondence over an arbitrarily large number of population specific 3D faces across identities, facial expressions and poses. The algorithm is initialized with a subset of anthropometric landmarks detected by our proposed Deep Landmark Identification Network which is trained on synthetic images. The landmarks are used to segment the 3D face into Voronoi regions by evolving geodesic level set curves. Exploiting the intrinsic features of these regions, we extract discriminative keypoints on the facial manifold to elastically match the regions across faces for establishing dense correspondence. Finally, we generate a Region based 3D Deformable Model which is fitted to unseen faces to transfer the correspondences. We evaluate our algorithm on the tasks of facial landmark detection and recognition using two benchmark datasets. Comparison with thirteen state-of-the-art techniques shows the efficacy of our algorithm.

Details

Title: Deep, dense and accurate 3D face correspondence for generating population specific deformable models
Authors/Creators: Syed Zulqarnain Gilani - Univ Western Australia, Sch Comp Sci & Software Engn, Nedlands, WA, Australia
Ajmal Mian - The University of Western Australia
Peter Eastwood - The University of Western Australia
Publication Details: Pattern recognition, Vol.69, pp.238-250
Publisher: Elsevier
Number of pages: 13
Grant note: APP1109057 / Australian National Health and Medical Research Council (NHMRC); National Health and Medical Research Council (NHMRC) of Australia
Identifiers: 991005592649007891
Murdoch Affiliation: Vice Chancellery
Language: English
Resource Type: Journal article

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Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.17 Computer Vision & Graphics; 4.17.118 Face Recognition
Web Of Science research areas: Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic
ESI research areas: Engineering