A Hybrid Model for Photographic Supra-Projection

Photographic supra-projection (CS) comes under forensic process in which video shots or photographs of a missing person are compared against the skull that is found. By projecting both photographs on top of each other (or, even better, matching a scanned 3-D skull model against the face photo/video shot), the forensic anthropologist can try to ascertain whether it is the same person. The overall process is affected by inherent uncertainty, mostly because two objects of different nature (a face and a skull ) are involved. In this paper, we extended existing evolutionary-algorithm-based techniques to automatically superimpose the 3-D skull model and the 2-D face photo with the aim to overcome the limitations that are associated with the different sources of uncertainty, which are present in the problem. Three different approaches to handle the imprecision will be proposed: Viola- Jones Face Detection Framework, Canonical Correlation Analysis and Inverse Compositional Active Appearance Model. DOI: 10.17762/ijritcc2321-8169.15076

Forensic Facial Reconstruction from Skeletal Remains

The identity of a skull in forensic is of critical importance. Forensic facial reconstruction is the reproduction of the lost or unknown facial features of an individual. In this paper, we propose the automation of the reconstruction process. For a given skull, a data-driven 3D generative model of the face is constructed using a database of CT head scans. The reconstruction can be constrained based on prior knowledge of parameters such as bone thickness measurements, cranial landmark distance measurements and demographics (age, weight, height, and BMI). The CT scan slices are segmented and a 3D model skull of 2D slices is generated with the help of Marching Cubes Algorithm. The 66 Landmark points are then calculated using Active Shape Models and PCA algorithm and placed on the skull. These Landmark points act as references for tissue generation. The facial soft tissue thickness is measured and estimated at the 66 craniometric landmarks used in forensic facial reconstruction. The skin mesh is generated using Delaunay automatic triangulation method. The performance of this model is then measured using RSME technique. The aim of this study is to develop a combination of techniques and algorithms to give the most accurate and efficient results

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Modeling skull-face anatomical/morphological correspondence for craniofacial superimposition-based identification

Craniofacial superimposition (CFS) is a forensic identification technique which studies the anatomical and morphological correspondence between a skull and a face. It involves the process of overlaying a variable number of facial images with the skull. This technique has great potential since nowadays the wide majority of the people have photographs where their faces are clearly visible. In addition, the skull is a bone that hardly degrades under the effect of fire, humidity, temperature changes, etc. Three consecutive stages for the CFS process have been distinguished: the acquisition and processing of the materials; the skull-face overlay; and the decision making. This final stage consists of determining the degree of support for a match based on the previous overlays. The final decision is guided by different criteria depending on the anatomical relations between the skull and the face. In previous approaches, we proposed a framework for automating this stage at different levels taking into consideration all the information and uncertainty sources involved. In this study, we model new anatomical skull-face regions and we tackle the last level of the hierarchical decision support system. For the first time, we present a complete system which provides a final degree of craniofacial correspondence. Furthermore, we validate our system as an automatic identification tool analyzing its capabilities in closed (known information or a potential list of those involved) and open lists (little or no idea at first who may be involved) and comparing its performance with the manual results achieved by experts, obtaining a remarkable performance. The proposed system has been demonstrated to be valid for sortlisting a given data set of initial candidates (in 62,5% of the cases the positive one is ranked in the first position) and to serve as an exclusion method (97,4% and 96% of true negatives in training and test, respectively)

Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks

Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intra-operative space. CT images are used in the pre-operative (planning) stage, whilst white light (TV camera) images are used to capture the intra-operative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a Polynomial Neural Network (PNN) classifier based on Gabor features for the detection and localisation of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks

The use of low-cost photogrammetry techniques to create an accurate model of a human skull

There has been increasing interest in low-cost close range photogrammetry techniques for use in a variety of applications. The use of these techniques in medicine, forensic science, architecture, engineering, archaeology and anthropology to record, measure and monitor objects and sites has been growing in recent years. Close range photogrammetry has been particularly investigated and preferred for human body mapping due to being non-contact, non-invasive, accurate, and inexpensive and data is re-measurable. Skulls have been traditionally measured using callipers and tape in anthropological study, which is subject to observer error. Close range photogrammetry can be used to perform more accurate measurements and retain a digital copy of the skull, which can be re-used for a number of purposes. Using low cost software (Photomodeler), and low cost cameras, the aim of this project is to detail the camera calibration techniques and image capture of a skull. The process for 3D modelling using close range photogrammetry includes camera calibration to determine the camera’s internal parameters, photographing the object within a control target frame, and processing the data with photogrammetry software. Achieving a high precision camera calibration and producing a high-accuracy 3D model were more difficult than anticipated. There are a number of factors which can result in a poor quality models. However, the results show that photogrammetry can be utilised in the capture of accurate 3D skull model using low-cost cameras efficiently. The research was successful, the project objectives were satisfied and the accuracy across the project was approximately 0.4mm