Acta Anthropologica Sinica ›› 2024, Vol. 43 ›› Issue (03): 427-439.doi: 10.16359/j.1000-3193/AAS.2023.0043
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ZHAO Ruimin(), LIU Kai, SUN Peng, ZHANG Zhongliang(
)
Received:
2023-04-26
Revised:
2023-07-23
Online:
2024-06-15
Published:
2024-06-04
CLC Number:
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.
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URL: https://www.anthropol.ac.cn/EN/10.16359/j.1000-3193/AAS.2023.0043
Fig.4 Patterns of thumb and forefinger fingerprint features with age in males and females correspond to the pattern of changes in dr, br, bv, nh, nv, L, ni, nf and P with age in the thumb and forefinger for males and females,and each feature value in the figure shows the median value of the data measured at each age, MT, FT, MF, FF stand for male thumb, female thumb, male index finger, and female index finger
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 |
Tab.1 F1 scores of different feature combinations based on 10 classification methods
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 |
Tab.2 F1 scores of 3 types of features under 10 classification methods
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 |
Fig.6 RAF method parameter tuning diagram the regression accuracy of the RAF method with the variation of parameters for 6 combinations of features from A to F
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% |
Tab. 3 Accuracy of RAF age regression without gender classification and after gender classification
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% |
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