Automatic cephalometric landmarks detection on frontal faces: An approach based on supervised learning techniques

Abstract

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).