Iris Texture Images Classification And Identification Based On Tree-Structured Wavelet Transform

Following the Internet and information technology development, the security consciousness of the people became more and more high and the claim of security became more and more high. At present, the routine security technology doesn't meet the requirement of security quality of the people, then the people turn to biology recognition technology to meet the requirement. For iris has some good advantage, such as reliability, unique, and it can't forge and it can't infringe, then iris recognition is put forward. Iris recognition can be widely used in security and customs, and it provides superiority security than other human feature recognition such as fingerprint, face and so on. The iris is complex enough to be used as a biometric signature with imposter odds ranging as high as 1 in 10. It means that the improbability of finding two people with identical iris pattern for identification, thus it is important to define a representation that is well adapted to extract the iris information context from iris texture images.In general, all source iris images are congener images, but when it comes to the iris area, Iris of different persons is different. This is the crucial precondition of iris identification. So we should first detect the iris area from the source iris image. In the course of this we adopt the following approach: detect the iris edge by Canny operator; estimate the center of pupil and size of inner edge of iris; and use the Hough transform to detect the inner and outer edges of iris on the consideration that their have the same center; finally, use Normalization Line Algorithm to transform the iris annular area to rectangular area. For the normalized iris texture images, if we find a effective way to extract their characters, then we can classify the iris images. The wavelet transform is a transform about time and frequency, so the information can be extracted by the wavelet transform. The mother wavelet has the translation and scale parameters, which change respectively. The sign can be analyzed into Multiresolution by the wavelet transform, and it can solve many difficult question which can't be solved by the Fourier transform. For the wavelet transform have the translation and scale propriety, then the wavelet transform is called "microscope". In the preceding method based on pyramid-structured wavelet transform, for the reason that the decomposition is implied recursively to the low frequency region, these approaches is very likely to loss much information in middle or high frequency region. As a result, the information extracted is sufficient enough for the latter identification. Some researchers attempted to decompose all this frequency regions to extract more information of the texture images. But this added the complication computation. In order to obtain the important information of the iris texture image and avoid a full decomposition. We consider a criterion to decide whether a further decomposition is needed for a particular output. Based on this realization, we give a newly approach of iris identification.As above statement, for the uncertainty of the number of subimages after decomposition every time, we should consider all-around or local evaluation. For example, there are four subimages to be chosen after the first decomposition, but the number of these subimages becomes sixteen after second decomposition, etc. the local evaluation: in this approach, we only choose the subimage to further decomposition from the four subimages of some subimage after decomposition every time. While in the all-around evaluation, the subimage to further decomposition is chosen from all subimages after decomposition every time. The subimage contains more texture information by the criterion. Obviously, we should adopt the latter approach; only in this way can we embody the adaptability of tree-structured wavelet transform. The decomposition of all-around evaluation is not easy to implement for uncertainty of the number of subimages and it can't be performed by recursive way. From our experime...