Agradecimentos: The authors would like to acknowledge the team of Federal Police of Brazil, specially the forensic experts of National Institute of Criminalistic. This work was conducted with financial support from Coordination for the Improvement of Higher Education Personnel (CAPES) and Federal...

Agradecimentos: The authors would like to acknowledge the team of Federal Police of Brazil, specially the forensic experts of National Institute of Criminalistic. This work was conducted with financial support from Coordination for the Improvement of Higher Education Personnel (CAPES) and Federal Police of Brazil (Pro-Forenses 001 Edital 25/2014 CAPES). Dr. Báñezs work has been supported by the Spanish MINECO Juan de la Cierva-Incorporación Fellowship IJCI-2014-22433

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...

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)

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