基于性别的指纹特征研究
Research on Gender-related Fingerprint Features
摘要：
指纹是人体的重要生物特征，其中包含着丰富的性别信息。目前学术界对指纹性别特征的研究普遍停留在认识层面，而标准化研究则相当有限。在这项工作中，我们提出了一种更强大的方法，密集扩张卷积ResNet（DDC-ResNet）从指纹中提取有效的性别信息。通过在骨干网中用无环卷积代替常规卷积，提供先验知识以保持边缘细节，并且可以扩展全局接收场。我们从三个方面探索了结果：1）DDC-ResNet的效率。在我们的数据集上评估了6种典型的自动特征提取方法与9种主流分类器的耦合，并给出了合理的实现细节。实验结果表明，我们的方法的组合优于其他组合的平均准确率和单独的性别准确率。平均识别率为96.5%，性别区分率为0.9752/0.9548。2)手指的效果。研究发现，右手无名指在不同手指的性别分类中表现最好。3)具体特征的影响。根据我们的方法可视化的指纹浓度的观察，可以推断，环和螺纹（1级），分叉（2级），以及线形（3级）与性别有关。最后，我们将开源包含6000个指纹图像的数据集
英文摘要：
Fingerprint is an important biological feature of human body, which contains abundant gender information. At present, the academic research of fingerprint gender characteristics is generally at the level of understanding, while the standardization research is quite limited. In this work, we propose a more robust method, Dense Dilated Convolution ResNet (DDC-ResNet) to extract valid gender information from fingerprints. By replacing the normal convolution operations with the atrous convolution in the backbone, prior knowledge is provided to keep the edge details and the global reception field can be extended. We explored the results in 3 ways: 1) The efficiency of the DDC-ResNet. 6 typical methods of automatic feature extraction coupling with 9 mainstream classifiers are evaluated in our dataset with fair implementation details. Experimental results demonstrate that the combination of our approach outperforms other combinations in terms of average accuracy and separate-gender accuracy. It reaches 96.5% for average and 0.9752 (males)/0.9548 (females) for separate-gender accuracy. 2) The effect of fingers. It is found that the best performance of classifying gender with separate fingers is achieved by the right ring finger. 3) The effect of specific features. Based on the observations of the concentrations of fingerprints visualized by our approach, it can be inferred that loops and whorls (level 1), bifurcations (level 2), as well as line shapes (level 3) are connected with gender. Finally, we will open source the dataset that contains 6000 fingerprint images
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