Automation of the assessment of craniofacial superimposition using soft computing and computer vision

Abstract

Within the forensic identi cation techniques, craniofacial superimposition is one of the most relevant skeleton-based approaches. It involves the process of overlaying a skull with one or more photographs of missing persons and the analysis of their morphological correspondence. This identi cation technique has a great application potentiality since nowadays the wide majority of the people have photographs (ante-mortem material) where their faces are clearly visible. The counterpart, the skull (post-mortem material), is a bone that hardly degrades with the e ect of re, humidity, high or low temperatures, time lapse, etc. Three consecutive stages for the whole CFS process have been distinguished [DCI+11]: 1) Acquisition and processing of the materials, the skull (the 3D model in the latest techniques) and the textitante-mortem facial photographs of the possible candidates; 2) Skull-face overlay, which focuses on achieving the best possible superimposition of the skull and a single ante-mortem image; 3) Decision making, where the degree of support that the skull and the face belong to the same person (positive identi cation) or not (exclusion) is determined. Soft computing and computer vision present certain characteristics that become it powerful tools to automate craniofacial superimposition, reducing the time taken by the expert and obtaining an unbiased overlay result. In particular, evolutionary techniques for 3D reconstruction of the skull are presented in [SCD07a, SCD+07b, SCD+09, C ACD13], highly useful for the rst stage. In addition, the automatic system proposed in [ICDS09, ICDS11, ICD12, CAIN+14] performs the skull-face overlay projecting the skull 3D model on the facial 2D image through a direct correspondence between cranial and facial landmarks using evolutionary algorithms and fuzzy sets. It also models the imprecision location of the facial landmarks in the photograph. This method has achieved promising results, however, the nal decision of the third stage is made manually by the forensic anthropologist in view of the superimposition obtained in the previous step. The aim of this PhD dissertation is to extend the functionality of the current automatic CFS procedure in order to develop a more reliable and robust computer-aided system. Concerning the second stage of the process, we have accomplished an extension of the existing automatic methods to superimpose a skull 3D model on a facial photograph by modeling the facial soft tissue depth between corresponding pairs of cranial and facial landmarks. This information is available in several anthropometric studies but its imprecise nature has caused it not to be considered in automatic skull-face overlay methods yet. Besides, a deep study for applying the most appropriate metrics in order to obtain the best possible superimposition has been performed. In the third stage, we propose a complete framework for a decision support system, which takes into account all the sources of information and uncertainty involved in the process. This decision support system has been developed using computer vision techniques and fuzzy logic, and it has been evaluated and validated with real positive and negative cases obtaining really good performance.