Research On Iris Recognition Theory

Iris recognition, as an important branch of biometric recognition, recognizes person'sidentity according to the textures of iris region in our eyes. Compared with otherbiometric identification technologies, iris recognition is much more stable, secure andanti-counterfeiting. In the past more than ten years after this technology was firstlyproposed, many related methods have been proposed for iris recognition. However, theexisting iris recognition methods also have some drawbancks, especially in the irisimage quality evaluation part and iris localization part. Aiming at solving the difficultproblems in these two parts, this thesis proposes a novel improved iris image blurdetection method and four effective iris localization algorithms.At the beginning of this thesis, we introduce the concepts of biometric recognitionand iris recognition. Moreover, we introduce the major parts of iris recongtion system,namely, iris image quality evaluation, iris localization, iris image normalization, irisocclusion mask estimation, feature extraction and matching as well as iris image coarseclassification. In the description for each part, we also introduce the related existingmethods and their advantages and disadvantages. To solve the problems in irisrecognition and improve the performance of exisiting methods, we have done thefollowing works:1. Propose the blurred iris image detection model with multiple kernel learning.Blurred iris image detection is an especially important problem for automatic irisrecognition system. However, it is a non-reference image quality evaluation problemand is hard to find discriminative features for blur detection. In this thesis, we analyzethe characteristics of the frequency spectrum and cepstrum of blurred iris image andpropose two new discriminative blur features, namely: Spectral Ennergy DensityDistribution and Singular Cepstrum Histogram. Defining blur feature in cepstrum isalso one of the major contributions of this thesis. To merge the two proposed blurfeatures, we employ multiple kernel learning theory to construct a merging kernel whichis a linear combinition of two kernels. The experiment results demonstrate the improvedperformance of our method.2. Propose a new iris localization model based on probabilistic pairwise voting. Iris localization is an especially hard and important task in iris recognition. As withother biometric recognition problems, correct localization or registration is key toaccurate iris recognition since it allows "apples-to-apples" comparisons. However, dueto the occlusion from eyelash, eyelid and spectral reflections, as well as the off-axiscapturing, iris localization is a very challenging problem. This thesis proposes a newcircular object detection method which can be used for iris localization. The proposedmethod is robust to small object shape deformations, noise and occlusions. The wholevoting model is formulated in continuous parameter space and the optimal parametercan be detected though using mode-finding mehods. This stratege reduces thecomputational cost of existing voting-based circle detection method. When applying foriris localization, the proposed method performs well.3. Propose a new iris localization model based on local experts of edge point andHough Clustring.For most existing iris localization methods, they usually utilize the gradientinformation of edge points to detect iris inner or outer edge. However, except thegradient value, there are also many other kind of local image patch features which canbe used for iris and non-iris edge point discrimination. In this thesis, we propose toextract the local image feature of every edge point and train two local experts todesriminate iris and non-iris inner or outer edge points. Meanwhile, coupled with thespatial distribution of these edge points, we can slectect the correct iris edge pointsthrough Hough Clustering. Finally, smooth spline fitting is adopted to fit these slectededge points to get the accurate edge of iris. Related experiment results show theimproved performance of this method.4. Propose a novel iris localization method based on Multi-resolution analysis andM-estimation.Multi-resolution analysis is an efficient image analysis theory. In this thesis, wesuppose the edges of pupil, iris, eyelash and eyelid are image features exsisting atdifferent scales. Then, we propose to extract iris edge points at appreciate scales toavoid the disturbing of eyelash and eyelid. Meanwhile, we propose to employM-estimation to fit these extracted edge points.5. Propose a new iris localization method based on phase congruency analysis andtrimmed least square fitting. Phase congruency analysis is an efficient method for image feature localization andextraction. This thesis proposes to detect the local maximum of phase congruency atapprociate scale range and choose these points with local maximal phase congruency aspupillary and iridial edge points. Detecting the edge points of iris based on phasecongruency analysis is robust to illumination change and contrast variations. Moreover,we propose to employ trimmed least square method to fit these edge points robustly.At the end of this thesis, we conclude the advantages and disadvantages of theproposed methods, then introduce the future work.