利用指纹特征分析人的性别及年龄，一直是法医学和人类学领域的挑战性项目。现有研究因统计样本量少、特征利用率低、模型学习能力低等不足，导致利用指纹特征分析性别及年龄的准确率低。本文从多种分类及回归机器学习模型比对分析的角度，对2980份拇指及食指指纹捺印样本（男性1500份，女性1480份）的指纹特征进行统计测量，并在拇指及食指不同指纹特征相互组合的情况下，观察各机器学习模型的性别分类、年龄回归的准确率。结果表明，同时使用拇指及食指指纹特征进行学习的性别分类、年龄回归的准确率高于使用单一手指指纹特征的准确率。其中，使用指纹特征对性别进行分类的结果中，F₁衡量指标值最高为0.979；对男性指纹样本进行年龄回归的结果中最高准确率为86.7%，女性指纹样本年龄回归的最高准确率为85.3%，证明使用拇指及食指指纹特征综合进行学习，可提高性别分类及年龄回归准确率思路的有效性。

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.