Automated craniofacial landmarks detection on 3D image using geometry characteristics information

• Main Authors: Arpah Abu, Chee Guan Ngo, Nur Idayu Adira Abu-Hassan, Siti Adibah Othman
• Format: Article
• Language: English
• Published: BMC 2019-02-01
• Series: BMC Bioinformatics
• Subjects: Indirect anthropometry; Automated craniofacial landmarks; 3D facial image; Geometry characteristics information
• Online Access: http://link.springer.com/article/10.1186/s12859-018-2548-9

Abstract Background Indirect anthropometry (IA) is one of the craniofacial anthropometry methods to perform the measurements on the digital facial images. In order to get the linear measurements, a few definable points on the structures of individual facial images have to be plotted as landmark points. Currently, most anthropometric studies use landmark points that are manually plotted on a 3D facial image by the examiner. This method is time-consuming and leads to human biases, which will vary from intra-examiners to inter-examiners when involving large data sets. Biased judgment also leads to a wider gap in measurement error. Thus, this work aims to automate the process of landmarks detection to help in enhancing the accuracy of measurement. In this work, automated craniofacial landmarks (ACL) on a 3D facial image system was developed using geometry characteristics information to identify the nasion (n), pronasale (prn), subnasale (sn), alare (al), labiale superius (ls), stomion (sto), labiale inferius (li), and chelion (ch). These landmarks were detected on the 3D facial image in .obj file format. The IA was also performed by manually plotting the craniofacial landmarks using Mirror software. In both methods, once all landmarks were detected, the eight linear measurements were then extracted. Paired t-test was performed to check the validity of ACL (i) between the subjects and (ii) between the two methods, by comparing the linear measurements extracted from both ACL and AI. The tests were performed on 60 subjects (30 males and 30 females). Results The results on the validity of the ACL against IA between the subjects show accurate detection of n, sn, prn, sto, ls and li landmarks. The paired t-test showed that the seven linear measurements were statistically significant when p < 0.05. As for the results on the validity of the ACL against IA between the methods, ACL is more accurate when p ≈ 0.03. Conclusions In conclusion, ACL has been validated with the eight landmarks and is suitable for automated facial recognition. ACL has proved its validity and demonstrated the practicability to be used as an alternative for IA, as it is time-saving and free from human biases.