Finger Vein Recognition Based On Feature Points And Subspace

With the development of information technology and the progress of human society, information security has become an increasingly thorny issue. Traditional identification is basically based on identity, such as documents, password, card number, but it can’t really verify that the user is not the user himself, once others falsely, will give the cardholder unnecessary losses. In order to overcome the defects, biometric identification technology gradually developed, such as fingerprints, palm prints, face, iris. Fingerprint identification, development is relatively mature, often used for employee attendance. However, because of the fingerprints, palm prints are in the human external, are susceptible to damage, such as hand labor, may be worn fingerprints, palm prints, with the passage of time may completely destroy the structure of fingerprints and palm prints, leading to registration is unsuccessful. Therefore, vein feature, as one of the in vivo biological features, become the first choice. Especially, finger vein recognition for its acquisition, flexible processing is known.In this dissertation, we are under the background of finger vein recognition technology, focusing on the feature matching methods based on finger vein feature points and subspace. The main tasks as follows:1、A new finger vein capturing device is designed, which has the valley points of the fingers fixed, and three finger vein (index finger、middle finger、ring finger) images database is built. There are a total of630finger vein images and21persons for each person30images (each finger10images).2、The work of finger vein image preprocessing, finger vein feature extraction and aft-processing was introduced. Finger vein image preprocessing contains region of interest (ROI) location, size and gray-scale normalized. In the part of the finger vein feature extraction, mainly introduces how to use finger vein pattern to the valley shape detection algorithm to split a finger vein patterns. Aft-processing is the work of wiping out the discrete points after the finger vein patterns divided and filling the void. Finally, skeletonize finger vein pattern image, get the skeleton of the finger vein patterns, and extract finger vein feature points.3、Due to feature matching method based on finger vein feature points, which only use Forward Mean Hausdorff Distance (FMHD) to characterize the distance from the matched finger vein feature points to the enrolled finger vein feature points. We propose a similarity measure between the new feature point sets based on Fisher Criterion, fusing the Forward and Reverse Mean Hausdorff Distance. The experiment results show that Recognition Rate (RR) respectively increases from54.81%to87.03%.4、In the part of feature matching method based on subspace, we introduces the basic theoretics of Principal Component Analysis (PCA), Maximum Margin Criterion (MMC), Linear Discriminat Analysis (LDA), then, use the method of One Dimensional Principal Component Analysis (1DPCA), Two Dimensional Principal Component Analysis (2DPCA), One Dimensional Maximum Margin Criterion (1DMMC), Two Dimensional Maximum Margin Criterion (2DMMC), One Dimensional Linear Discriminat Analysis (1DLDA), Two Dimensional Linear Discriminant Analysis (2DLDA) and2DPCA+2DPCA、2DMMC+2DMMC、2DLDA+2DLDA2DPCA+2DMMC、2DPCA+2DLDA、2DMMC+2DLDA to extract finger vein feature and match them. The experiment results show that Recognition Rate (RR) of three feature matching methods,2DPCA+2DLDA、2DMMC+2DLDA and2DLDA+2DLDA is the highest, but also they ensure that recognition time and compressive performance are maintained about1s and100dimensional space respectively.