Research On Biometric Recognition Method Based On Hand Vein And Iris

With the rapid development of modern network and information technology, the safety requirements for social information are constantly increased, and personal identification based on biometrics has received extensive attention. In the biometrics family, hand vein and iris recognitions are characteristic by stability, security, and un-intrusion, which are two research focuses and can be widly applied to many fields such as government, military, bank and electronic commerce. Hand vein recognition is a newly emerging identification technology, and its study and application are at the preliminary stage. Iris recognition technology has developed fast and become mature in recent years, whereas further study about kernel algorithms is still needed.Firstly a hand vein recognition prototype system is developed. Centering on this goal, we design a near-infrared (NIR) hand vein imaging system and construct Tianjin Universty (TJU) hand vein database. Three hand vein recognition methods are developed based on the multi-resolution texture feature, algebraic feature and local SIFT feaure. The proposed recognition methods for hand vein are tested and compared with the classical methods, which demonstrate their feasibility and effectiveness. Then the key iris algorithms are studied including iris localization, eyelid localization and iris feature extraction. Eventually, the paper preliminarily discusses the multi-biometric fusion based on the hand vein and iris at the match score level.The major innovations of the dissertation are as follows:1. A hand vein recognition method based on multi-resolution texture feature analysis is proposed. Wavelet transform is applied to describe the vein texture variety, the influence of wavelet function on recognition performance is analyzed by experiments, and the tolerance to hand shift and rotation is evaluated.2. A hand vein recognition method based on local SIFT feature is developed. The SIFT feature is adopted to describe the gradient information of hand vein, and the improved matching method for SIFT features enhances the identification performance. This algorithm is not sensitive to hand shift and rotation, which makes it has important practical significance.3. A rapid iris localization algorithm based on image sampling is advanced. Its basic idea is to locate the iris coarsely in the sampled image, and achieve the exact localization in the image with original resolution. Much disturbing information can be removed to reduce the computation complexity, and the real-time performance is improved greatly.4. An eyelid detection method based on maximal connection path is developed. After determining the eyelid detection region and enhancing the horizontal boundary, the maximal connection path is searched as the eyelid edge points through labeling the connected paths in the edge image, and then the eyelid segmentation can be achieved. This method increases the detection speed largely.5. The hand vein and iris recognitions are combined for personal identification. The D-S evidence theory and support vector machines (SVM) are applied to fusion experiment and analysis at the match score level, and the recognition performance has been improved greatly.