2021
Age verification using random forests on facial 3D landmarks
JANDOVÁ, Marie; Marek DAŇKO a Petra URBANOVÁZákladní údaje
Originální název
Age verification using random forests on facial 3D landmarks
Autoři
JANDOVÁ, Marie; Marek DAŇKO a Petra URBANOVÁ
Vydání
Forensic Science International, Clare, Elsevier Ireland Ltd, 2021, 0379-0738
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
30501 Forensic science
Stát vydavatele
Irsko
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 2.676
Kód RIV
RIV/00216224:14310/21:00120827
Organizační jednotka
Přírodovědecká fakulta
UT WoS
000608606000003
EID Scopus
2-s2.0-85097228322
Klíčová slova anglicky
age verification; age estimation; 3D facial models; random forests; FIDENTIS Database
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 14. 4. 2022 11:23, Mgr. Marie Novosadová Šípková, DiS.
Anotace
V originále
Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and verification. This paper proposes an approach using random forests, a machine learning method, to develop and test models for classification of legal age thresholds (15 years and 18 years) using 3D facial landmarks. Our approach was developed on a set of 3D facial scans from 394 Czech individuals (194 males and 200 females) aged between 10 and 25 years. The dataset was retrieved from a sizable database of Central European faces – The FIDENTIS 3D Face Database. Three main types of input variables were processed using random forests: I) shape (size-invariant) coordinates of 3D landmarks, II) size and shape coordinates of 3D landmarks, and III) inter-landmark distances, angles and indices. The performance rates for the combinations of variables and age threshold were expressed in terms of sensitivity and specificity. The overall accuracy rates varied from 71.4% to 91.5% (when the male and female samples were pooled). In general, higher accuracy was achieved for the age limit of 18 years than for 15 years. Whereas size-variant variables showed a better performance rate for the age limit of 15 years, the size-invariant variables (i.e., shape variables) were better for classifying individuals under 18 years. The verification models grounded on traditional variables (distances, angles, indices) yielded consistently higher performance rates on females than on males, whereas the inverse trend was observed for the models built on 3D coordinates. The results indicate that age verification based on 3D facial data with processing by the random forests method has high potential for further forensic or biometric applications.
Návaznosti
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