<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthew Caruana</style></author><author><style face="normal" font="default" size="100%">Joseph G. Vella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Facial Reconstruction  from 2D Portrait Imagery</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">digital forensics</style></keyword><keyword><style  face="normal" font="default" size="100%">Forensic Facial Reconstruction</style></keyword><keyword><style  face="normal" font="default" size="100%">Landmark Alignment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">328-340</style></pages><abstract><style face="normal" font="default" size="100%">3D facial images are reconstructed from 2D portraits using regression trees for facial landmark alignment and 3D morphable models. Two generic regression trees were adopted, one being based on the widely used 68-landmark structure, and the other based on a 74-landmark structure. The FaceWarehouse dataset was used to create a novel 74-landmark regression tree and during the system’s evaluation. The accuracy of the models generated was computed through the Root Mean Square, 75th Percentile and Arithmetic Mean comparison metrics. Two different datasets of 2D images were reconstructed. The evaluation results demonstrate that a higher level of accuracy and precision was attained from the models reconstructed using 68-landmark regression tree when compared to the 74 developed here. The accuracy produced by the 68-landmark regression tree applied to two sets was 85 % and 90 % as opposed to the 82 % and 83 % produced by the 74-landmark regression tree on the same model subsets; thus justifying its wide adoption.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><section><style face="normal" font="default" size="100%">328</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neil Farrugia</style></author><author><style face="normal" font="default" size="100%">Joseph Vella</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automating Footwear Impressions Retrieval through Texture</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">digital forensics</style></keyword><keyword><style  face="normal" font="default" size="100%">digital image processing</style></keyword><keyword><style  face="normal" font="default" size="100%">footwear impressions</style></keyword><keyword><style  face="normal" font="default" size="100%">texture-based similarity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">73-86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This study aims to provide an automatic footwear extraction and correlation system. The artefact proposed is able to apply pre-processing, extract key features and retrieve the relevant matches from a footwear impression repository.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; In order to compare images, a comparison function was utilised. This function creates a MPEG-1 movie out of the images and employs the size of the movie in order to calculate the similarity. For pre-processing of the prints, apart from common techniques, an original concept of tessellations was applied. A publicly available dataset of footprints, a subset of which come from crime scenes, was used. The results obtained from the development of this project have shown that the accuracy of matching depends on the quality of the images that are being used. Comparisons are done in two batches: first, all crime scene prints (i.e. 170) are compared with all the reference prints (i.e. 1175), then the procedure is repeated with various pre-processing methods being applied to the input prints. Accuracy averaged at 55 % (without pre-processing) and at 65 % for a particular method of pre-processing (i.e. based on 43 prints).&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">73</style></section></record></records></xml>