Using fingerprint characteristics of a person’s thumb and forefinger to analyze his gender and age

doi:10.16359/j.1000-3193/AAS.2023.0043

Abstract

Abstract:

The analysis of gender and age by using fingerprint characteristics has been a persistent challenge in the fields of forensic medicine and anthropology. Relevant studies have many shortcomings and deficiencies that need to be improved. For example, the number of fingerprint samples is insufficient, the utilization rate of fingerprint characteristics is low, and the learning ability of the model used is low, all of which will lead to the decline of the accuracy of using fingerprint characteristics to analyze individual gender and age. To address these limitations, this study adopts a multi-classification approach and performs a comparative analysis of regression machine learning models. A comprehensive analysis was conducted on a dataset comprising 2980 thumb and forefinger fingerprint samples, consisting of 1500 male and 1480 female individuals. Statistical measurements were performed on the fingerprint characteristics of the dataset, and the accuracy of gender classification and age regression for each machine learning model was observed. The models were tested by combining different fingerprint characteristics from the thumb and forefinger. The findings reveal that utilizing both thumb and forefinger fingerprint characteristics significantly improves the accuracy of gender classification and age regression compared to using single finger fingerprint characteristics. Notably, the highest F₁ score achieved for gender classification using fingerprint characteristics was 0.979, indicating a remarkably high accuracy level. For male fingerprint samples, the highest accuracy in age regression reached 86.7%, while for female fingerprint samples, it yielded a highest accuracy of 85.3%. These outcomes validate the efficacy of comprehensive learning with thumb and forefinger fingerprint characteristics in enhancing gender classification and age regression accuracy. The results of this study contribute significant insights to the application of fingerprint characteristics in determining gender and age. By addressing the limitations of previous research and emphasizing the importance of multi-classification and comparative analysis, it demonstrates the potential for achieving higher accuracy in gender and age analysis through the integration of thumb and forefinger fingerprint characteristics. These findings hold profound implications for the fields of forensic medicine and anthropology, offering valuable support for future research and practical applications in fingerprint feature analysis. With continued advancements in technology and further research, it is anticipated that the application of fingerprint characteristics in gender and age analysis will continue to evolve and improve. The comprehensive understanding derived from this study serves as a foundation for future investigations, encouraging the exploration of enhanced methodologies and refining the accuracy and reliability of gender and age analysis through fingerprint characteristics.

Key words: fingerprint, machine learning, gender, age

CLC Number:

Q983

ZHAO Ruimin, LIU Kai, SUN Peng, ZHANG Zhongliang. Using fingerprint characteristics of a person’s thumb and forefinger to analyze his gender and age[J]. Acta Anthropologica Sinica, 2024, 43(03): 427-439.

Figures/Tables 11

References 29

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方法Method→ 分组groups↓	RAF	ADB	CTB	EXT	KNN	BPN	SVM	XGB	LGB	BYS
1	0.949	0.951	0.954	0.941	0.938	0.907	0.550	0.942	0.948	0.910
2	0.951	0.954	0.956	0.950	0.946	0.909	0.583	0.949	0.955	0.901
3	0.956	0.955	0.955	0.949	0.944	0.911	0.648	0.950	0.961	0.915
4	0.958	0.954	0.957	0.956	0.949	0.924	0.642	0.957	0.959	0.912
5	0.958	0.954	0.962	0.955	0.953	0.923	0.557	0.953	0.964	0.921
6	0.963	0.960	0.962	0.957	0.951	0.928	0.751	0.961	0.964	0.914
7	0.959	0.963	0.966	0.964	0.957	0.929	0.547	0.961	0.966	0.930
8	0.964	0.959	0.971	0.964	0.953	0.925	0.710	0.962	0.967	0.916
9	0.975	0.967	0.971	0.963	0.954	0.929	0.706	0.957	0.973	0.924

方法Method→ 分组groups↓	RAF	ADB	CTB	EXT	KNN	BPN	SVM	XGB	LGB	BYS
1	0.949	0.951	0.954	0.941	0.938	0.907	0.550	0.942	0.948	0.910
2	0.951	0.954	0.956	0.950	0.946	0.909	0.583	0.949	0.955	0.901
3	0.956	0.955	0.955	0.949	0.944	0.911	0.648	0.950	0.961	0.915
4	0.958	0.954	0.957	0.956	0.949	0.924	0.642	0.957	0.959	0.912
5	0.958	0.954	0.962	0.955	0.953	0.923	0.557	0.953	0.964	0.921
6	0.963	0.960	0.962	0.957	0.951	0.928	0.751	0.961	0.964	0.914
7	0.959	0.963	0.966	0.964	0.957	0.929	0.547	0.961	0.966	0.930
8	0.964	0.959	0.971	0.964	0.953	0.925	0.710	0.962	0.967	0.916
9	0.975	0.967	0.971	0.963	0.954	0.929	0.706	0.957	0.973	0.924

特征Feature→ 方法 Method↓	拇指Thumb	食指Forefinger	拇指食指组合特征Thumb & Forefinger
RAF	0.975	0.966	0.979
ADB	0.967	0.963	0.969
CTB	0.971	0.963	0.973
EXT	0.963	0.951	0.967
KNN	0.954	0.938	0.962
BPN	0.939	0.921	0.941
SVM	0.716	0.679	0.712
XGB	0.957	0.934	0.965
LGB	0.973	0.956	0.954
BYS	0.914	0.905	0.938

特征Feature→ 方法 Method↓	拇指Thumb	食指Forefinger	拇指食指组合特征Thumb & Forefinger
RAF	0.975	0.966	0.979
ADB	0.967	0.963	0.969
CTB	0.971	0.963	0.973
EXT	0.963	0.951	0.967
KNN	0.954	0.938	0.962
BPN	0.939	0.921	0.941
SVM	0.716	0.679	0.712
XGB	0.957	0.934	0.965
LGB	0.973	0.956	0.954
BYS	0.914	0.905	0.938

特征Feature→	男性Male			女性Female
方法Method↓	拇指Thumb	食指Forefinger	拇指食指组合特征Thumb& Forefinger	拇指Thumb	食指Forefinger	拇指食指组合特征Thumb& Forefinger
RAF	75.8%	72.1%	82.9%	78.5%	73.6%	84.5%
ADB	75.2%	72.3%	86.7%	77.1%	74.5%	85.3%
EXT	74.6%	71.1%	85.3%	77.8%	72.4%	81.3%
CTB	71.3%	69.2%	81.6%	75.7%	70.9%	81.1%
KNN	67.8%	65.5%	72.1%	73.2%	68.3%	78.9%
SVR	59.6%	53.7%	68.4%	73.4%	70.6%	80.8%
LGB	73.2%	72.0%	84.2%	75.6%	71.4%	81.2%
MLR	68.5%	67.3%	75.2%	71.3%	69.3%	80.5%