Department of Anthropology, Faculty of Natural Sciences, Comenius University, Ilkovičova 6, Mlynská dolina, Bratislava, 84215, Slovakia.
Department of Criminalistics and Forensic Sciences, Academy of Police Forces, Sklabinská 8414/1, Bratislava, 83517, Slovakia.
J Forensic Sci. 2020 Jul;65(4):1303-1309. doi: 10.1111/1556-4029.14299. Epub 2020 Feb 19.
Minutiae are small distinguishing features found along every ridge flow, which make each friction ridge print unique. The most common friction ridge prints found at the crime scene are fingerprints; therefore, the most of the minutiae studies are focused exactly on this kind of prints. The authors believe that further examination and enlargement of the palm print database could result in better use of the palm prints for personal identification. We analyzed a total of 160 palm prints from 40 females and 40 males aged between 18 and 70 years from Slovakia. For the evaluation of the minutiae, the area of the hypothenar had to be marked out. The classification of the minutiae used for this study was based on a modified version of the classification system using the total of 13 types of minutiae. The frequency of every minutiae type was calculated and, using the chi-square test with Yates's correction, bilateral and sex differences were assessed. The relationship between the different types of minutiae was examined with Pearson's correlation test. During the initial phases of the identification process, the focus should be on the least common types of minutiae (Y or M and return), which were found not to correlate; thus, their mutual occurrence is random (e.g., overlap-Y or M, crossbar-return, or Y or M-dock). The results of the present study show which specific minutiae types are the most suitable for personal identification. These findings may be beneficial in more effective outcome of the identification process.
细节特征是在每条脊纹流中发现的微小区别特征，使每个摩擦脊纹印痕独一无二。在犯罪现场发现的最常见的摩擦脊纹印痕是指纹；因此，大多数细节特征研究正是集中在这种印痕上。作者认为，进一步检查和扩大掌纹数据库，可能会导致更好地利用掌纹进行个人识别。我们分析了来自斯洛伐克的 40 名女性和 40 名男性的总共 160 个掌纹，年龄在 18 至 70 岁之间。为了评估细节特征，必须标记出手心区域。用于本研究的细节特征分类是基于使用总共 13 种细节特征的分类系统的修改版本。计算了每种细节特征类型的频率，并使用带有 Yates 校正的卡方检验评估双侧和性别差异。使用 Pearson 相关检验检查不同类型细节特征之间的关系。在识别过程的初始阶段，重点应该放在最不常见的细节特征类型（Y 或 M 和返回）上，这些特征类型被发现没有相关性；因此，它们的相互出现是随机的（例如，重叠 Y 或 M、横杆返回或 Y 或 M 码头）。本研究的结果显示了哪些特定的细节特征类型最适合个人识别。这些发现可能有助于更有效地进行识别过程。
