Performance evaluation of memetic approaches in 3D reconstruction of forensic objects

Different tasks in forensics require the use of 3D models of forensic objects (skulls, bones, corpses, etc.) captured by 3D range scanners. Since a whole object cannot be completely scanned in a single image using a range scanner, multiple acquisitions from different views are needed to supply the information to construct the 3D model by a range image registration method. There is an increasing interest in adopting evolutionary algorithms as the optimization technique for image registration methods. However, the image registration community tends to separate global and local searches in two different stages, named sequential hybridization approach, which is opposite to the scheme adopted by the memetic framework. In this work, we aim to analyze the capabilities of memetic algorithms (Moscato in On evolution, search, optimization, genetic algorithms and martial arts: towards memeticalgorithms. Report 826, Caltech Concurrent Computation Program, Pasadena, 1989) for tackling a really complex and challenging real-world problem as the 3D reconstruction of forensic objects. Our intention is threefold: firstly, designing new memetic-based methods for tackling a real-world problem and subsequently carrying out a performance and behavioral analysis of the results; secondly, comparing their performance with the one achieved by other methods based on the classical sequential hybridization approach; and thirdly, concluding the experimental study by highlighting the outcomes achieved by the best method in tackling the real-world problem. Several real-world 3D reconstruction problems from the Physical Anthropology Lab at the University of Granada, Spain, were used to support the evaluation study.