Research Of Finger-vein Pattern Extraction Algoirthm

With the vigorous development of the socio-economy, the requirements of people forinformation security are increasingly high. Due to the disability to meet the growingdemands for identification, the traditional authentication methods gradually withdrawfrom the stage of history, replaced by a new generation biometrics. The fingerprintrecognition technology as an important member of biometric technology has a maturedevelopment. But subjecting to a constraint upon its own security and stability, thecurrent development has entered a bottleneck stage. In recent years, finger-veinrecognition technology as a substitute of fingerprint recognition technology, graduallygains people’ recognition. Compared to fingerprint recognition technology, the veinrecognition has a better stability: the imaging of finger-veins would not be affected byhuman traumas, wears and other factors. It also has a higher security: veins are thebody’s internal characteristic, so there is no opportunity for criminals to forge theveins. It is the series of advantages that make the finger-vein recognition technologythe new darling of the scholars’ research.This paper does a series of studies focusing on the pattern extraction algorithms offinger-veins. This paper first introduces the acquisition of finger-veins images.Because the deoxyhemoglobin in blood can absorb near-infrared light, the finger-veins can be imaged under the near-infrared light. This article uses four classic imagesegmentation methods to segment finger-veins image, and the experiments show thatthe treatment effect is not very satisfactory. Consequently this paper uses thealgorithm of maximum curvature to extract the patterns of finger-veins, whichprocesses an image from five angles of its section where the vein is located and finallygets a good experiment result. In this paper, the most widely used Canny edgedetection algorithm is improved by using bilateral filter instead of the Gauss filter. Itwill denoise more thoroughly and get a better edge detection result on the basis of noloss of information energy. The synthesis algorithm of finger-vein images is alsoresearched deeply. The distribution laws of the veins in real image are summarized,according to which the vein curves are fitted, with the method of least squares. Andthen these vein curves are syncretized with the synthetic vein background mode to getthe synthesis finger-vein image. At last, this article studies the characteristics extraction of the finger-vein image. The invariance of Hu moments is verified andused to extract the features of vein images.The study of this article is mainly focused on the following areas:In the first part, this article describes the significance of finger-vein recognitiontechnology, the research status of domestic and international and the marketdevelopment. And then points out that the vein recognition technology has a broadprospect for development following the fingerprint recognition. Finally, the mainstudy contents and the framework of this paper are introduced.In the second part, the acquisition method of finger-vein image is described, referringthat the vein is easy to image in the near-infrared irradiation which because thehemoglobin in the blood vessels is easy to absorb the light in this wavelength range. Aseries of pre-treatments is done after obtaining the finger-vein image, includingadjustments to the image’s histogram to make it balanced, and normalization the grayof image to facilitate further processing of the image. Four classic thresholdsegmentation algorithms are used to segment the image: the fixed threshold method,the overall average method, the OTSU method and the threshold image segmentationmethod. The results of experiments show that the above four algorithms can not dealwith the finger-vein image effectively. The reason is that the gray-scale contrast ofvein image is not very strong and there is a lot of noise in the image, resulting in thedescent of image quality.In the third part, this article uses the maximum curvature method to process the imageaccording to the poor quality of vein image. At first, the basic principles of maximumcurvature algorithm are introduced. The location profile of finger-vein image isobtained. The analysis find out that the gray value of the vein declines from the bothsides to the central part of the vein, and it will reach the local minimum at the centerline. Then the concept of curvature is decided to use to search for the center point ofvein. The position profiles are got from five directions which are0°,30°,60°,120°and150°. After each of them is processed respectively, the center line is obtained bygathering the five directions’. The complete vein pattern is extracted after skeletonand refinement.In the fourth part, the Canny edge detection algorithm is improved based on thebilateral filter. The processes of traditional Canny edge detection algorithm are firstintroduced: filtering by Gauss filters, followed by non-maxima suppression andfinally the method of hysteresis thresholding is used to connect the edge. This paper proposes the concept of range filters and domain filters. By combining those twofilters, the bilateral filter is obtained which is able to remove the noise without loss ofsignal energy. This article uses bilateral filter to replace the Gauss filter in the Cannyedge detection algorithm, and uses an inverted Gaussian model to extract the veincenterline.The algorithm of synthesis finger-vein image is introduced in the fifth part. First, thedistribution laws of real finger-vein image are summarized, in accordance with which,the vein curves are fitted using the least squares method. And then the100real finger-vein images are accumulated. The mean value is used as the background mode ofsynthesis finger-vein image. With strengthen coefficients and attenuation coefficientsas weights, vein patterns and background mode are syncretized. By adjusting theparameters, it can simulate the vein pattern under a variety of circumstances. There isa great significance for the evaluation and optimization of vein processing algorithms.In part six, the feature extraction of finger-vein image is studied. This article uses thetheory of Hu invariant moment to extract the features of finger-vein image, which canavoid the influences caused by compressions, offsets, uneven illuminations and otherfactors on venous imaging. First, the validation is done on the Hu moments to provethe invariance of stretching, translation and rotation. Then the Hu invariant momentsare used to extract the features of finger-vein.The seventh part summarizes the paper, and looks ahead to the next phase of work.