基于深度学习的小面积指纹匹配方法

针对传统的基于细节特征点的指纹匹配方法多适用于采集面积较大的指纹，在面向智能手机端的小采集面积指纹时准确率明显下降的问题，提出一种基于深度学习的小面积指纹匹配方法。首先，提取指纹图像的细节特征点信息；其次，搜索和标定感兴趣纹理区域（ROI）；然后，构建并改进基于残差结构的轻量级深度神经网络，通过采用二值化特征模式优化网络和Triplet Loss方式训练模型；最后，制定一种智能手机端注册-匹配策略实现小面积指纹匹配。实验结果表明，提出方法在公开库FVCDB1与自建数据库上的等错率（EER）分别仅为0.50%与0.58%，远低于传统的基于细节特征点的指纹匹配方法，能够有效提升小面积指纹匹配的性能，更好地满足智能手机端的应用需求。

Small-size fingerprint matching based on deep learning

Focused on the issue that the traditional fingerprint matching methods based on minutiae are mainly applicable for large-size fingerprint and the accuracy rate would reduce significantly when dealing with small-size fingerprint from smart phone, a small-size fingerprint matching method based on deep learning was proposed. Firstly, the detailed information of minutiae was extracted from fingerprint images. Secondly, the Regions Of Interest (ROI) were searched and labeled based on minutiae. Then a lightweight deep neural network was built and improved from original residual module. In addition, binary feature pattern and triplet loss were used to optimize and train the proposed model respectively. Finally, the small-size fingerprint matching was accomplished with the fusion strategy of registration and matching. The experimental results show that the Equal Error Rate (EER) of the proposed method can reach 0.50% and 0.58% on public FVC_DB1 and in-house database respectively, which is much lower than the traditional fingerprint matching methods based on minutiae, and can improve the performance of small-size fingerprint matching effectively and meet the requirements on smart phone.