Application of Deep Learning in Fingerprint Recognition
Fingerprint recognition is one of the earliest and most mature biometric recognition technologies that is widely used in mobile payments,access control and attendance in the civilian field,and in criminal investigation to retrieve clues from suspects.Recently,deep learning technology has achieved excellent application results in the field of biometric recognition,and provided fingerprint researchers with new methods for automatic processing and the application of fusion features to effectively represent fingerprints,which have excellent application results at all stages of the fingerprint recognition process.This paper outlines the development history and application background of fingerprint recognition,expounds the main processing processes of the three stages of fingerprint recognition,which are image preprocessing,feature extraction,and fingerprint matching,summarizes the application status of deep learning technology in specific links at different stages,and compares the advantages and disadvantages of different deep neural networks in specific links,such as image segmentation,image enhancement,direction field estimation,minutiae extraction,and fingerprint matching.Finally,some of the current problems and challenges in the field of fingerprint recognition are analyzed,and future development directions,such as building public fingerprint datasets,multi-scale fingerprint feature extraction,and training end-to-end fingerprint recognition models,are prospected.
万方数据
维普
