Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Centre of Research for Computational Sciences and Informatics for Biology, Bioindustry, Environment, Agriculture and Healthcare, University of Malaya, 50603, Kuala Lumpur, Malaysia.
BMC Bioinformatics. 2019 Feb 4;19(Suppl 13):548. doi: 10.1186/s12859-018-2548-9.
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).
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.
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.
间接测量法（IA）是对数字面部图像进行测量的颅面测量方法之一。为了获得线性测量值，必须在个体面部图像的结构上绘制几个可定义的点作为标志点。目前，大多数人类学研究使用的标志点是由检查者手动绘制在三维面部图像上的。这种方法耗时且存在人为偏差，当涉及到大数据集时，不同的检查者之间的偏差会有所不同。有偏差的判断也会导致测量误差更大。因此，本研究旨在实现标志点检测的自动化，以帮助提高测量的准确性。在这项工作中，使用几何特征信息开发了一个 3D 面部图像系统的自动颅面标志点（ACL），以识别鼻根（n）、前鼻突（prn）、下鼻突（sn）、鼻翼（al）、上唇（ls）、人中（sto）、下唇（li）和唇珠（ch）。这些标志点是在.obj 文件格式的 3D 面部图像中检测到的。IA 也是通过使用 Mirror 软件手动绘制颅面标志点来完成的。在这两种方法中，一旦所有的标志点都被检测到，然后提取八个线性测量值。通过比较从 ACL 和 AI 提取的线性测量值，对 ACL（i）在受试者之间和（ii）在两种方法之间的有效性进行了配对 t 检验。测试是在 60 名受试者（30 名男性和 30 名女性）中进行的。
ACL 对 IA 的受试者间有效性的结果显示，n、sn、prn、sto、ls 和 li 标志点的检测准确。配对 t 检验显示，当 p<0.05 时，七个线性测量值具有统计学意义。至于 ACL 对 IA 方法间有效性的结果，当 p≈0.03 时，ACL 更准确。
总之，ACL 已经通过了 8 个标志点的验证，适合于自动面部识别。ACL 已经证明了其有效性，并展示了作为 IA 的替代方法的实用性，因为它节省了时间，且不存在人为偏差。
