Iris Plaque-like Textures Detection

There are plenty of textures on the surface of human irises, which are consisted of crypts,plaques, furrows and circle lines. The feature information contained in iris textures is thesignificant basis for iris identification and iris diagnosis. At present, the existing iris texturesdetection methods cannot offer the detailed characteristics of the iris textures, such as size,location, morphology and category, but these characteristics are the effective supplement ofthe existing feature of iris identification systems and can also provide the texture featurerequired for iris diagnosis systems.In order to solve the problems discussed above, this thesis studied the approaches of iristextures detection for specific type. According to the shape characteristics, four kinds of iristextures were divided into three categories: plaque-like textures, line textures and circletextures. The crypts and plaques were classified as the plaque-like textures, the furrows as theline textures, and the circle lines as the circle textures. Focusing on the plaque-like textures,the thesis studied the detection methods and the classification methods of crypts and plaques.The main contributions of this thesis are as follows:(1) For the problem that the existing iris texture feature extraction methods can not obtainthe features of the specific type of textures, such as the size, shape and position, a new idea ofiris textures feature extraction was proposed. The iris textures were divided into threecategories. By detecting the specific kind of textures, the geometric and located information ofthe iris texture was acquired to use as the iris texture feature. These features of space domaincan supplement the existing frequency methods in the iris identification systems. In the irisdiagnosis systems, these features can be used to get diagnosis.(2) For the problem of the inefficiency of iris plaque-like textures detection, threeapproaches were proposed. These approaches can detect the targets in the iris whether it issimple background only with plaques or complex background with multi-kinds of texturescoexisting. These methods are availiable to different environments and the detectioncomplexity is their merits. Following the coarse to fine ideas, the plaque-like textures existingin the iris image could be detected so that the feature parameters of the plaque-like texturescan be acquired when used in iris recognition systems or iris diagnosis systems. Method I: To solve the problem of plaque-like textures detection of the irises only withplaques, a method based on local gray minimum and level set was proposed. Firstly, accordingto the gray distribution of the plaque-like textures, the local gray minimums were searched forin the preprocessed iris image to find the probable plaque regions. Therefore, the plaques wereinitially located. And then, the regions’ edge contours were evolved with level set method.Finally, by judging the size and closing feature of the contours, the plaques were detectedamong them. The test results show that the method proposed is effective for plaque-liketextures detection of the irises only with plaques, and the accuracy was92.45%.Method II：To solve the problem of plaque-like textures detection of the irises withmulti-kinds of textures, a method based on combinational windows searching was proposed. Aseries of variable size combinational windows was designed based on the gray distribution andthe shape of the plaque-like textures. The preprocessed iris images were searched by thesewindows and the regions in which the plaque-like textures existed were found out and theother regions were excluded. And then, with OTSU method, the regions acquired weresegmented and the plaque-like textures were detected. The integrated accuracy of this methodwas90.59%.Method III: To solve the same problem, a method based on texture energy parameter andedge shape factor was proposed. According to the characteristics of the plaque-like textures, aregion texture energy parameter was defined in this thesis. By using it, the regions containingthe plaque-like, linear or annular textures were detected out by Support Vector Machine, andthe plaques were initially located. By analyzing the edge information of the initialized location,according to the shape of the plaque-like textures, an edge shape factor special for thedetecting target was defined. By using it, the edge contours of the plaque-like textures wereextracted from the edge image, and the plaque-like textures were detected. The integratedaccuracy of this method was85.7%.(3) To solve the problem of plaque-like textures classification, an approach of detectionand classification of crypts and plaques was proposed. First, the probable plaque-like existingregions were initially located by using clustering method based on the gray of the pixels. Andthen, the adjacent regions were connected by close operation. Finally, according to the graydistribution of crypts and plaques, two region feature parameters, i.e. gray standard deviationand average variation rate of close operation, were defined to constitute the feature vector of Support Vector Machine, by which the crypts, plaques and other textures were classified. Thedetection and classification accuracy were87.39%and95.84%respectively.(4) In order to obtain the feature information of the plaque-like textures, according totheir characteristics, the area, perimeter, long axis length, short axis length and barycentercoordinates were selected to be feature parameters to describe the morphology and the positionof the plaque-like textures. The computational formulas of these parameters were proposed,and the feature vector and matrix of iris plaque-like textures were defined.Moreover, the problems of standard classification gallery establishment and thepreprocessing of iris images were discussed in this thesis. The standard gallery of differenttypes of iris textures was established. According to the saturation component of the colored irisimage, the interference of light was removed from the visible light iris images.