Level 3 details: the biological variability among the Chinese Population

Abstract Fingerprint evidence plays a critical role in forensic identification. While prevailing methodologies rely predominantly on level 1 (patterns) and level 2 (minutiae) features, the potential of level 3 details, involving ridge shapes and pore structures/locations has been overlooked, particularly in the case of fragmentary, distorted, or blurred latent prints. Despite their forensic significance, research on the level 3 details within the Chinese population remains underexplored. This study evaluates both the variability and distribution of level 3 details in the Chinese population. A dataset consisting of 2,000 fingerprints from 200 donors (aged 11–46 years), collected via cyanoacrylate, ninhydrin, and standard 10-print card methods, is studied. Analytical results reveal that the frequencies of incipient ridges and ridge breaks are 24.1% and 3.9%, respectively, with ridge breaks occurring most frequently on thumbs, while incipient ridges correlate strongly with whorls. Although no significant gender correlation is observed, males exhibit a higher incidence of incipient ridges. Statistical modelling confirms Poisson-distributed occurrences for both ridge breaks and incipient ridges (P < 0.01), and significant correlations are also found for intra-finger and inter-finger incipient ridges. These findings establish robust empirical baselines for level 3 details and support their integration into forensic evidence evaluation, increasing the discriminative power of latent prints.

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Publication Details

Journal
Law Probability and Risk
Published
2025-01-01
DOI
https://doi.org/10.1093/lpr/mgaf005
Primary Topic
Dermatoglyphics and Human Traits
Type
article
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article

Level 3 details: the biological variability among the Chinese Population

Zhixia Cao, Xiyue Wang, Jian Wu, Jingying Liu et al.
Law Probability and Risk
Dermatoglyphics and Human Traits
article

Level 3 details: the biological variability among the Chinese Population

Zhixia Cao, Xiyue Wang, Jian Wu, Jingying Liu, Yaping Luo
article en

Abstract

Abstract Fingerprint evidence plays a critical role in forensic identification. While prevailing methodologies rely predominantly on level 1 (patterns) and level 2 (minutiae) features, the potential of level 3 details, involving ridge shapes and pore structures/locations has been overlooked, particularly in the case of fragmentary, distorted, or blurred latent prints. Despite their forensic significance, research on the level 3 details within the Chinese population remains underexplored. This study evaluates both the variability and distribution of level 3 details in the Chinese population. A dataset consisting of 2,000 fingerprints from 200 donors (aged 11–46 years), collected via cyanoacrylate, ninhydrin, and standard 10-print card methods, is studied. Analytical results reveal that the frequencies of incipient ridges and ridge breaks are 24.1% and 3.9%, respectively, with ridge breaks occurring most frequently on thumbs, while incipient ridges correlate strongly with whorls. Although no significant gender correlation is observed, males exhibit a higher incidence of incipient ridges. Statistical modelling confirms Poisson-distributed occurrences for both ridge breaks and incipient ridges (P < 0.01), and significant correlations are also found for intra-finger and inter-finger incipient ridges. These findings establish robust empirical baselines for level 3 details and support their integration into forensic evidence evaluation, increasing the discriminative power of latent prints.

Law Probability and RiskVol. 24(1)
China People's Public Security University (CN), Ministry of Interior (HU), Zhejiang Provincial Public Security Department (CN), Chinese People's Armed Police Force Engineering University (CN), Henan Radio and Television University (CN), Northeastern University (CN)
Openalex Percentile: Top 77%
Dermatoglyphics and Human Traits
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Level 3 details: the biological variability among the Chinese Population — Zhixia Cao, Xiyue Wang, et al. · Law Probability and Risk (2025) | TGRS Research Map | TGRS