3D Face Reconstruction with Mobile Phone Cameras for Rare Disease Diagnosis

Yiwei Liu, Ling Li, Senjian An, Petra Helmholz, Richard Palmer, Gareth Baynam

Research output: Chapter in Book/Conference paperConference paperpeer-review


Computer vision technology is advancing rare disease diagnosis to address unmet needs of the more than 300 million individuals affected globally; one in three rare diseases have a known facial phenotype. 3D face model reconstruction is a key driver of these advances. However, the utility of 3D reconstruction from images obtained from mobile phone cameras has been questionable due to relatively low quality 2D data and need external calibration methods (e.g. visual markers) to extract accurate measurements. Herein a novel implementation pipeline, leveraging deep learning technologies, that can successfully reconstruct 3D face models from multiple 2D images taken by mobile phone cameras for clinician usage is described. Specifically, Multi-view Stereo (MVS) has been introduced to this application for providing a cost-effective pipeline of 3D face dense reconstruction. As a state-of-the-art MVS method, deep-learning based MVS has shown its strong generalization capability of using the low quality 2D face images to reconstruct 3D face models without camera calibration. The results demonstrate conceptual proof of a analytic pipeline to satisfy the clinician’s needs.
Original languageEnglish
Title of host publicationAI 2022
Subtitle of host publicationAdvances in Artificial Intelligence - 35th Australasian Joint Conference, AI 2022, Proceedings
EditorsHaris Aziz, Débora Corrêa, Tim French
Place of PublicationSingapore
Number of pages13
ISBN (Electronic)9783031226946
Publication statusPublished - 2022
Event35th Australasian Joint Conference on Artificial Intelligence, AI 2022 - Perth, Australia
Duration: 5 Dec 20229 Dec 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13728 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference35th Australasian Joint Conference on Artificial Intelligence, AI 2022


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