Facial landmarks are employed in many research areas, including facial recognition, craniofacial identification, age and sex estimation being the most important. In forensics, the focus is on the analysis of a particular set of facial landmarks, defined as cephalometric landmarks. Previous studies demonstrated that the descriptive adequacy of these anatomical references for indirect application (photo-anthropometric description) increased the marking precision of these points, contributing to greater reliability of these analyses. Nevertheless, most are performed manually and all are subject to bias on the part of expert examiners. Therefore, the purpose of this work was to develop and validate automatic techniques for detection of cephalometric landmarks from digital images of frontal facial images in forensics. The presented approach uses a combination of computer vision and image processing techniques within supervised learning procedures. The proposed methodology obtains similar precision to a group of human manual cephalometric reference markers and results that are more accurate than other state-of-the-art facial landmark detection frameworks. It achieves a normalized mean distance (in pixels) error of 0.014, similar to the mean inter-expert dispersion (0.009) and clearly better than other automatic approaches that were analyzed during the course of this study (0.026 and 0.101).

面部标志被用于许多研究领域，其中最重要的是面部识别，颅面识别，年龄和性别估计。在司法鉴定中，重点是分析一组特定的面部标志，这些标志被定义为头颅测量标志。先前的研究表明，这些解剖学参考文献对于间接应用（光人体测量学描述）的描述性充分性提高了这些点的标记精度，从而有助于提高这些分析的可靠性。但是，大多数操作都是手动执行的，所有这些操作都会受到专家检查员的偏见。因此，这项工作的目的是开发和验证自动技术，以从法医中的正面面部图像的数字图像中检测头影界标。提出的方法在监督学习过程中结合了计算机视觉和图像处理技术。所提出的方法获得了与一组人类手动头颅测量参考标记相似的精度，并且其结果比其他最新的面部地标检测框架更准确。它实现了0.014的归一化平均距离（以像素为单位）误差，类似于专家之间的平均离散度（0.009），并且明显好于本研究过程中分析的其他自动方法（0.026和0.101）。所提出的方法获得了与一组人类手动头颅测量参考标记相似的精度，并且其结果比其他最新的面部地标检测框架更准确。它实现了0.014的归一化平均距离（以像素为单位）误差，类似于专家之间的平均离散度（0.009），并且明显好于本研究过程中分析的其他自动方法（0.026和0.101）。所提出的方法获得了与一组人类手动头颅测量参考标记相似的精度，并且其结果比其他最新的面部地标检测框架更准确。它实现了0.014的归一化平均距离（以像素为单位）误差，类似于专家之间的平均离散度（0.009），并且明显好于本研究过程中分析的其他自动方法（0.026和0.101）。