An automatic approach for classification and categorisation of lip morphological traits

Abbas, Hawraa H., Hicks, Yulia ORCID: https://orcid.org/0000-0002-7179-4587, Zhurov, Alexei ORCID: https://orcid.org/0000-0002-5594-0740, Marshall, David, Claes, Peter, Wilson-Nagrani, Caryl and Richmond, Stephen ORCID: https://orcid.org/0000-0001-5449-5318 2019. An automatic approach for classification and categorisation of lip morphological traits. PLoS ONE 14 (10) , e0221197. 10.1371/journal.pone.0221197

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Abstract

Classification of facial traits (e.g., lip shape) is an important area of medical research, for example, in determining associations between lip traits and genetic variants which may lead to a cleft lip. In clinical situations, classification of facial traits is usually performed subjectively directly on the individual or recorded later from a three-dimensional image, which is time consuming and prone to operator errors. The present study proposes, for the first time, an automatic approach for the classification and categorisation of lip area traits. Our approach uses novel three-dimensional geometric features based on surface curvatures measured along geodesic paths between anthropometric landmarks. Different combinations of geodesic features are analysed and compared. The effect of automatically identified categories on the face is visualised using a partial least squares method. The method was applied to the classification and categorisation of six lip shape traits (philtrum, Cupid’s bow, lip contours, lip-chin, and lower lip tone) in a large sample of 4747 faces of normal British Western European descents. The proposed method demonstrates correct automatic classification rate of up to 90%.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Dentistry Computer Science & Informatics Engineering
Additional Information:	This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publisher:	Public Library of Science
ISSN:	1932-6203
Date of First Compliant Deposit:	16 October 2019
Date of Acceptance:	1 August 2019
Last Modified:	11 Oct 2023 18:40
URI:	https://orca.cardiff.ac.uk/id/eprint/126075

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