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ST-OPEN, Vol. 1 , 2020.

Izvorni znanstveni članak

https://doi.org/10.48188/so.1.4

Sex estimation from handprints in a Croatian population sample: developing a tool for sex identification in criminal investigations

Andrea Kolić ; Sveučilišni odjel za forenzične znanosti, Sveučilište u Splitu, Split, Hrvatska
Ivan Jerković orcid id orcid.org/0000-0003-4716-9375 ; Sveučilišni odjel za forenzične znanosti, Sveučilište u Splitu, Split, Hrvatska
Šimun Anđelinović ; Sveučilišni odjel za forenzične znanosti, Sveučilište u Splitu, Split, Hrvatska


Puni tekst: engleski pdf 1.600 Kb

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Sažetak

Aim: To test if handprint measurements show sexual dimorphism in the Croatian population, and to develop population-specific sex estimation standards.

Methods: A cross-sectional study was conducted on 100 adult volunteers from a Croatian population (50 males and 50 females) aged between 20 and 45 years. Using a fingerprint ink, we collected handprints of both hands on a paper sheet. We scanned handprints and took 13 measurements. Bilateral asymmetry and sexual dimorphism of the measurements was analyzed and sex estimation models were developed using linear discriminant analysis.

Results: All measurements exhibited statistically significant sexual dimorphism (P<0.001). Univariate discriminant functions provided sexing accuracy from 75% to 92%. The highest accuracy rate (92%) and the lowest sexing bias (0%) was obtained using the handprint breadth. A multivariate discriminant function could estimate sex with 93% accuracy, but with more pronounced sexing bias (10%).

Conclusion: We showed that handprint measurements could be used for sex estimation in the Croatian population with a high accuracy level. Therefore, they could serve as a valuable tool for biological profiling of perpetrators in criminal investigations when other evidence is not conclusive.

Ključne riječi

hand impression; sex estimation; crime scene investigation; forensic science

Hrčak ID:

244499

URI

https://hrcak.srce.hr/244499

Datum izdavanja:

18.5.2020.

Posjeta: 1.183 *