基于HRF-Net的指纹细节点及汗孔统一提取方法

Fingerprint features have three levels of different characteristics, namely first-level features (shape and direction of ridges, etc.). second-level features (minutiae, etc.). and third- level features (pores, etc.). Traditional fingerprint recognition systems typically rely only on first and second-level features, particularly minutiae. Fingerprint recognition technology based on the fusion of multi-level features (such as minutiae, pores, etc.) has greatly improved the security and robustness of fingerprint recognition systems. Sweat pores are a crucial aspcct of high-res- olution fingerprint image recognition. However, current high-resolution fingerprint recognition technology predominantly focuscs on the sweat pore feature as a third-level characteristic, often overlooking other significant features present in fingerprint images. To address this issue, this articlc introduces the High-Resolution Fingerprint Net (HRF-Net) as a feature extraction model, which utilizes fingerprint images to generate heat maps of minutiae and sweat pores. These heat maps are then processed using a sliding window algorithm to obtain the coordinates of feature points. In the HRF-Net model, the introduction of intermediate outputs structure allows for the separation of sweat pore and minutiae features. Additionally, a staged supervision approach, starting from coarsc to fine, is employed to ensure the network learns different levels of features cffcctivcly. To rcducc computational complexity, a shuffle unit module is incorporated into the network, enabling efficient and accuratc extraction of fingerprint features at various levels. By generating heat maps of minutiae and sweat pores, it captures the intricate details of the fingerprint, enabling a more comprehensive representation of the features. The introduction of intermediate outputs structure allows for the disentanglement of sweat pore and minutiae features, contributing to a more focuscd and refined feature extraction. Additionally, the staged supervision approach ensures that the network learns the different levels of features progressively, enabling a holistic understanding of the fingerprint image. Furthermore, the incorporation of the shuffle unit module reduccs the computational complexity of the model. The combination of these techniques results in a highly efficient and accurate fingerprint feature extraction model. Experimental results show that our proposed unified extraction method achieves a true positive rate of 96. 59% in pore extraction, which is 3. 45.% higher than the best-performing Judge CNN. The true positive rate of minutiae extraction reachcs 81. 93.%. At the same time, we also achieved the best results in separate extraction of pores and minutiae. The Fl-score for the extraction of pores reaches 95. 83 %. which is 1. 48% higher than that of Judge CNN. We use the extracted features to conduct matching experiments on the fingerprint matching dataset, and achieve an equal error rate (EER) of 5. 39 % which is 7. 02% reduction compared to traditional methods. These results indicate that our proposed HRF-Net model delivers superior performance in pore and minutiae extraction, as well as matching accuracy. By leveraging the extracted features, our method significantly enhances the efficiency and reliability of fingerprint recognition systems. The HRF-Net model holds great potential for applications in biomctric security and forensics. offering a promising solution for high-resolution fingerprint feature extraction and matching.