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

Campomanes-Alvarez, C, Martos-Fernandez, R, Wilkinson, CM, Ibanez, O and Cordon, O (2018) Modeling skull-face anatomical/morphological correspondence for craniofacial superimposition-based identification. IEEE Transactions on Information Forensics and Security. ISSN 1556-6013

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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).

Item Type: Article
Additional Information: (c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: 08 Information And Computing Sciences, 09 Engineering
Subjects: Q Science > QA Mathematics > QA76 Computer software
Q Science > QM Human anatomy
Divisions: Art & Design
Publisher: IEEE Publishing
Date Deposited: 11 Jan 2018 12:30
Last Modified: 04 Sep 2021 03:26
DOI or ID number: 10.1109/TIFS.2018.2791434
URI: https://researchonline.ljmu.ac.uk/id/eprint/7808
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