Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> A novel formulation of the circle Hough transform (CHT) is introduced which enables the detection of circles of different radii to be made using a single parameter space. We formulate the CHT as one or more convolution operations. Comparisons are made between conventional CHT techniques, formulated as filters, and a novel complex vector CHT. This technique exploits both edge orientation and distance from an edge point coded as complex phase. We present results that show that the novel phase and orientation CHT has a positional accuracy of approximately +or-2 pixels at good signal to noise ratios (SNR) down to approximately -16 dB. Using orientation alone, the positional accuracy is poorer falling to approximately +or-10 pixels but at SNR levels of approximately -25 dB. > <s> BIB001 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan. <s> BIB002 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> As all emerging biometric for human identification, iris recognition has received increasing attention in recent years. This paper makes an attempt to reflect shape information of the iris by analyzing local intensity variations of an iris image. In Our framework, a set of one-dimensional (1D) intensity signals is constructed to contain the most important local variations of the original 2D iris image. Gaussian-Hermite moments of Such intensity signals reflect to a large extent their various spatial modes and are used as distinguishing features. A resulting high-dimensional feature vector is mapped into a low-dimensional subspace using Fisher linear discriminant, and then the nearest center classifier based on cosine similarity measure is adopted for classification. Extensive experimental results show that the proposed method is effective and encouraging. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. <s> BIB003 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> This paper presents a novel iris recognition system using 1D log polar Gabor wavelet and Euler numbers. 1D log polar Gabor wavelet is used to extract the textural features, and Euler numbers are used to extract topological features of the iris. The proposed decision strategy uses these features to authenticate an individual's identity while maintaining a low false rejection rate. The algorithm was tested on CASIA iris image database and found to perform better than existing approaches with an overall accuracy of 99.93%. MONG the present biometric traits, iris is found to be the most reliable and accurate (1). The use of human iris as a biometric feature offers many advantages over other biometric features. Iris is the internal human body organ that is visible from outside, but well protected from external modifiers. It has epigenetic formation and it is formed from the individual DNA, but a large part of its final pattern is developed at random. Two eyes from the same individual, although are very similar, contain unique patterns. Similarly, identical twins would exhibit four different iris patterns. These characteristics make it attractive for use as a biometric feature to identify individuals. Pattern recognition and image processing algorithms can be used to extract the unique patterns of iris from an eye image and encode it into an iris template. This iris template contains mathematical representation of the unique information stored in the iris and allows comparisons to be made between templates. Since 1990s, many researchers have worked on this problem. Human iris recognition process is basically divided into four steps, • Localization: Inner and outer boundaries of the iris are extracted. <s> BIB004 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> In this paper, we propose a 2-stage preprocessing framework which consists of image enhancement and deformation techniques to increase the verification performance of image-based biometric systems. In the preprocessing framework, first the quality of biometric image is enhanced and then a deformation model is applied to minimize the variation between the two images to be matched. The proposed SVM image quality enhancement algorithm selects good quality regions from different globally enhanced images and combines them to generate a single high-quality feature-rich image. The proposed deformation algorithm is based on phase congruency information and locally minimizes the variations between two images while retaining the features required for recognition. The proposed algorithms are validated using face and iris biometrics as the two case studies. For performance evaluation, different face and iris recognition algorithms are chosen and the verification accuracy is computed using non-homogenous face and iris databases. Experimental results show that the performance of face and iris recognition algorithms are significantly improved when the input images are preprocessed using the proposed enhancement and deformation algorithms. <s> BIB005 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases. <s> BIB006 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> The key concern of indexing is to retrieve small portion of database for searching the query. In the proposed paper iris database is indexed using energy histogram. The normalised iris image is divided into subbands using multiresolution DCT transformation. Energy based his- togram is formed for each subband using all the images in the database. Each histogram is divided into fixed size bins to group the iris images having similar energy value. The bin number for each subband is obtained and all subands are traversed in Morton order to form a global key for each image. During database preparation the key is used to traverse the B tree. The images with same key are stored in the same leaf node. For a given query image, the key is generated and tree is traversed to end up to a leaf node. The templates stored at the leaf node are retrieved and compared with the query template to find the best match. The proposed indexing scheme is showing considerably low penetration rate of 0.63%, 0.06% and 0.20% for CASIA, BATH and IITK iris databases respectively. <s> BIB007 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> This paper proposes an efficient indexing technique which can be used in an identification system with large multimodal biometric database. In this technique, multi-dimensional feature vectors of each trait (iris, signature, ear and face) are normalised and projected to a lower dimensional feature space. The reduced feature vectors are fused at feature level and used to index the database by forming Kd-tree. The performance of the proposed technique is also analysed with the feature vectors of all traits by first fusing them and projecting the fused feature vector to a lower dimensional space, and using it for indexing. Performance is also compared with the indexing based on score-level fusion. The experiment is performed on a multimodal database consisting of 5400 images of 150 subjects (i.e. nine images per subject, per trait). Out of the nine, eight images are used for training and one is used for testing. Our experiment shows that the proposed technique significantly reduces the data retrieval time along with possible error rates. <s> BIB008 </s> Iris Indexing Techniques: A Review <s> Based on Iris Texture <s> Performing identification on large-scale biometric databases requires an exhaustive linear search. Since biometric data does not have any natural sorting order, indexing databases, in order to minimize the response time of the system, represents a great challenge. In this work we propose a biometric hash generation technique for the purpose of biometric database indexing, applied to iris biometrics. Experimental results demonstrate that the presented approach highly accelerates biometric identification. <s> BIB009
An efficient indexing scheme based on energy histogram obtained from the iris texture was proposed in BIB007 . Image is stored into the database along with a key during enrolment. A B-Tree data structure was used to store the key value. The acquired iris image is then pre-processed using Circular Hough transform BIB001 and transformed into rectangular block BIB002 . The transformed rectangular region is enhanced to improve the texture details and make it illumination in variant. Next, the features are extracted using multiresolution DCT (Discrete Cosine Transformation)  . The input image is divided into non-overlapping 8x8 pixel block based on DCT approach. The co-efficient from each block belong to a particular subband are grouped together and a feature vector value is obtained form 10 such subbands. Further, during indexing energy histogram is built for each subband. The histogram generated from each subband is divided into bin to form logical groups. The bin number is used to form key value for indexing. Insertion to the B-Tree is made at the leaf level and the key for the image is calculated by finding the bin number. The tree is traversed from the root node down to the leaf node to find the suitable match. It has been observed that scalability is a problem in this approach as the size of the bin and subband is fixed which can lead to misclassification when there is coarse texture. This paper BIB008 proposed an efficient indexing technique which is used for a large multimodal biometric database. Iris was one of the traits focused in the above work and the feature vectors were normalized and projected to a lower dimensional feature vector space. The inner boundary of the iris was extracted using Circular Hough transformation BIB001 and the outer boundary based on intensity variation approach BIB003 . Then the iris feature was transformed into rectangular block and the features are extracting using Harr Wavelet Transform  . Later feature normalization was done and the feature vectors were used to form kd-tree. To find the similar matching the tree is traversed starting its search from the root and finds its position. A range of search result is produced and Euclidean distance is computed to identify the correct match. The above technique has outperformed in terms of accuracy, does but not holds good for large-scale database as the tree has to be traversed for a minimum time, based on the subset. A feature based iris indexing algorithm for non-ideal poorquality iris image was proposed in BIB006 . In this approach segmentation was done at two stages, as the method is for nonideal image. The first step in above approach is to calculate approximately the inner and outer boundaries using elliptical model. The second step is to figure out the exact inner and outer boundaries of the iris using modified Mumford-shah functional  . The detected iris is then unwrapped into a rectangular region by converting into polar co-ordinates. To enhance the image the standard enhancement algorithm were used. The enhanced local regions are identified from each of the transformed images using a support vector machine based approach BIB005 and a single high quality image was generated. The textural feature and topological features BIB004 are extracted using Euler number. Later the textural and topological match scores are fused for further classification and indexing is performed. The indexing is done by a two-step process. The first step is used to generate a subset of possible match list and in the second step using 2v-svm match score fusion was used, to find the best match from the possible matches. The result when compared with existing algorithm based on Euler code and 2v-svm which proved to have better result in terms of accuracy which is 97.21 % and time which the is less than 2s approximately. A new method for indexing iris images based on the context clustering property of the borrows-wheeler transform (BWT) was proposed  . The indexing scheme is based on the distribution of the patterns present in iris texture. The image acquisition and segmentation was based on the algorithm proposed by Shah & Ross and normalization done based on the Daugman's rubber sheet model BIB002 . In next step, the normalized gray scale iris is converted into a binary image. A horizontal n-bit patter is chosen with the least value of coefficient of variation. The list is then sorted accordingly and a modified binary search algorithm is used. These patterns are found in the iris image and then the normalized iris image is divided into vertical segments based on the maximum occurrence of the n-bit patterns among these segments. An index value is assigned based on the segment number. The approach seems to work well with small scale databases. Indexing based on hashing technique which do not sort iris template but on the other hand uses lower-dimensional hashes which are directly generated from the iris data, has also been proposed in BIB009 . In this technique the acquired image is preprocessed by discarding the top and bottom quarter say 315˚ to 45˚ and 135˚ to 225˚ of the iris, as they are often affected by eyelids and eyelashes. The preprocessed texture is used to extract a hash. Once the template is acquired a hash and a pointer is generated. The pointer generated is stored at the according node. If equal hashes were computed for different users, a linked list of pointer was stored at the according node. Thus the node can be reached by short paths. This technique addresses the problem of coarse classification and the hit rate is found to be better than other methods. The limitation in hashing technique is its scalability and it operates as a pure key generation and has no further user-specific secondary data.