A Unified Method for Minutiae and Sweat Pore Extraction Using HRF-Net
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 securi-ty and robustness of fingerprint recognition systems.Sweat pores are a crucial aspect of high-res-olution fingerprint image recognition.However,current high-resolution fingerprint recognition technology predominantly focuses on the sweat pore feature as a third-level characteristic,often overlooking other significant features present in fingerprint images.To address this issue,this article 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 coarse to fine,is employed to ensure the network learns different levels of features effectively.To reduce computational complexity,a shuffle unit module is incorporated into the network,enabling efficient and accurate 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 out-puts structure allows for the disentanglement of sweat pore and minutiae features,contributing to a more focused and refined feature extraction.Additionally,the staged supervision approach ensures that the network learns the different levels of features progressively,enabling a holistic understand-ing of the fingerprint image.Furthermore,the incorporation of the shuffle unit module reduces the computational complexity of the model.The combination of these techniques results in a highly effi-cient 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 reaches 81.93％.At the same time,we also achieved the best results in separate extraction of pores and minutiae.The F1-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 fin-gerprint matching dataset,and achieve an equal error rate(EER)of 5.39％which is 7.02％reduc-tion compared to traditional methods.These results indicate that our proposed HRF-Net model de-livers superior performance in pore and minutiae extraction,as well as matching accuracy.By levera-ging the extracted features,our method significantly enhances the efficiency and reliability of finger-print recognition systems.The HRF-Net model holds great potential for applications in biometric se-curity and forensics,offering a promising solution for high-resolution fingerprint feature extraction and matching.
万方数据
维普
