Laboratory of Morphology and Forensic Anthropology, Department of Anthropology, Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic.
Forensic Sci Int. 2021 Jan;318:110612. doi: 10.1016/j.forsciint.2020.110612. Epub 2020 Nov 21.
Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and verification. This paper proposes an approach using random forests, a machine learning method, to develop and test models for classification of legal age thresholds (15 years and 18 years) using 3D facial landmarks. Our approach was developed on a set of 3D facial scans from 394 Czech individuals (194 males and 200 females) aged between 10 and 25 years. The dataset was retrieved from a sizable database of Central European faces - The FIDENTIS 3D Face Database. Three main types of input variables were processed using random forests: I) shape (size-invariant) coordinates of 3D landmarks, II) size and shape coordinates of 3D landmarks, and III) inter-landmark distances, angles and indices. The performance rates for the combinations of variables and age threshold were expressed in terms of sensitivity and specificity. The overall accuracy rates varied from 71.4%-91.5% (when the male and female samples were pooled). In general, higher accuracy was achieved for the age limit of 18 years than for 15 years. Whereas size-variant variables showed a better performance rate for the age limit of 15 years, the size-invariant variables (i.e., shape variables) were better for classifying individuals under 18 years. The verification models grounded on traditional variables (distances, angles, indices) yielded consistently higher performance rates on females than on males, whereas the inverse trend was observed for the models built on 3D coordinates. The results indicate that age verification based on 3D facial data with processing by the random forests method has high potential for further forensic or biometric applications.
三维面部图像越来越普及。由于这些图像提供了比二维图像更多的面部形态信息，因此它们在未来的法医应用中具有很大的应用潜力，包括年龄估计和验证。本文提出了一种使用随机森林的方法，这是一种机器学习方法，用于使用 3D 面部地标开发和测试用于分类法定年龄阈值（15 岁和 18 岁）的模型。我们的方法是基于一组来自 394 名捷克个体（194 名男性和 200 名女性）的 3D 面部扫描数据进行开发的，年龄在 10 至 25 岁之间。该数据集来自中欧人脸的大型数据库 - FIDENTIS 3D 人脸数据库。使用随机森林处理了三种主要类型的输入变量：I）3D 地标形状（尺寸不变）坐标，II）3D 地标大小和形状坐标，以及 III）地标间距离、角度和指数。变量和年龄阈值的组合的性能率用灵敏度和特异性表示。总体准确率从 71.4％到 91.5％不等（当男性和女性样本合并时）。一般来说，18 岁的年龄限制比 15 岁的年龄限制获得更高的准确率。虽然尺寸变量在 15 岁的年龄限制下表现出更好的性能率，但尺寸不变的变量（即形状变量）在分类 18 岁以下的个体方面更好。基于传统变量（距离、角度、指数）的验证模型在女性中的表现率始终高于男性，而在基于 3D 坐标构建的模型中则观察到相反的趋势。结果表明，基于 3D 面部数据并通过随机森林方法处理的年龄验证在未来的法医或生物识别应用中具有很高的潜力。
