There are many population-specific studies around the world on sex estimation from skeletal remains. Of the long bones, the tibia has been an important one because it is commonly studied to assess population specificity of a long bone. However, the studies in Europe that use this bone for sex estimation remain limited. The aim of this study is to analyse the tibia in different populations of the southern Europe such as Greece, Italy, and Spain providing standards for sex estimation in a forensic context. In total, we analyzed tibiae of 157 Greek, 190 Italian, and 105 Spanish individuals. Standard osteometric measurements were taken and the data was analyzed using discriminant function statistics. Posterior probabilities were calculated for all produced formulae. Statistical analysis was performed using SPSS subroutines. All measurements were significantly different between the sexes in all three populations and in the pooled sample. A discriminant function of the pooled sample for Southern Europeans resulted in about 88 % accuracy using all three variables. Over 43 % of the individuals were correctly classified at a 0.95 threshold. More work should be done including other Southern European populations to this database to further test the applicability of the method.

The present study introduces a new approach to computer-assisted face/skull matching used for personal identification purposes in forensic anthropology. In this experiment, the authors formulated an algorithm able to identify the face of a person suspected to have disappeared, by comparing the respective person's facial image with the skull radiograph. A total of 14 subjects were selected for the study, from which a facial photograph and skull radiograph were taken and ultimately compiled into a database, saved to the hard drive of a computer. The photographs of the faces and corresponding skull radiographs were then drafted using common photographic software, taking caution not to alter the informational content of the images. Once computer generated, the facial images and menu were displayed on a color monitor. In the first phase, a few anatomic points of each photograph were selected and marked with a cross to facilitate and more accurately match the face with its corresponding skull. In the second phase, the above mentioned cross grid was superimposed on the radiographic image of the skull and brought to scale. In the third phase, the crosses were transferred to the cranial points of the radiograph. In the fourth phase, the algorithm calculated the distance of each transferred cross and the corresponding average. The smaller the mean value, the greater the index of similarity between the face and skull.A total of 196 cross-comparisons were conducted, with positive identification resulting in each case. Hence, the algorithm matched a facial photograph to the correct skull in 100% of the cases.

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