Iris Indexing Techniques: A Review <s> Based on Iris Code <s> The authors report on a scalable implementation of geometric hashing on the Connection Machine. Using this implementation, it is possible to recognize models consisting of patterns of points embedded in scenes, independent of translation, rotation, and scale changes. With 1024 models and a scene of 200 points, the implementation yields an execution time of 70 milliseconds for recognition of the one embedded model given a two-point basis set (consisting of a pair of points in the model) on a 64 K-processor CM-2 parallel computer. The algorithm is scalable, yielding an expected execution time that is O(log/sup 2/ M+log S log M) on a Mn/sup 3/-processor hypercube-connected SIMD machine such as the Connection Machine; M is the number of models, n is the number of points per model, S is the number of scene points, and it is assumed that execution proceeds until the first model is discovered. > <s> BIB001 </s> Iris Indexing Techniques: A Review <s> Based on Iris Code <s> Given a query iris image, the goal of indexing is to identify and retrieve a small subset of candidate irides from the database in order to determine a possible match. This can significantly improve the response time of iris recognition systems operating in the identification mode. In this work, we analyze two different approaches to iris indexing. The first technique is based on the analysis of IrisCodes (post-encoding indexing); the second technique is based on the analysis of features extracted from the iris texture (pre-encoding indexing). Experiments on a subset of the publicly available CASIA-IrisV3 database compare the two approaches and illustrate the potential of the proposed indexing methods for large scale iris identification. <s> BIB002 </s> Iris Indexing Techniques: A Review <s> Based on Iris Code <s> The PC-based software programming used in complex or luxuriant image processing algorithms is time consuming and resource wasting. As appropriate processing for the image data indeed speedups complicated algorithms, we focus on a crucial case - multilayered processes. In this paper, we gauge deeply into the data flow of multilayered image processing to avoid waiting for the result from every previous steps to access the memory which occurs in many applicable algorithms. Based on combining the parallel and pipelined properties to eliminate unnecessary delays, we propose new visual pipeline architecture and use field programmable gate array to implement our hardware scheme. For verification, the multiscale Harris corner detector in cooperating with shape context and thin-plate splines were combined to complete our real-time experiment of the integrated hardware and software (H/S) system for pattern recognition. <s> BIB003 </s> Iris Indexing Techniques: A Review <s> Based on Iris Code <s> This paper proposes an efficient indexing scheme for searching large iris biometric database that achieves invariance to similarity transformations, illumination and occlusion. The proposed scheme considers local descriptors as well as relative spatial configuration for claiming identity. To overcome the effect of non-uniform illumination and partial occlusion due to eyelids, local features are extracted from noise independent annular iris image using scale invariant feature transform (SIFT). The detected keypoints are used to index iris database by applying geometric hashing scheme that is robust to similarity transformations as well as occlusion. During iris retrieval, geometric hashed location from query iris image is obtained to access the appropriate bin of hash table and for every entry found there, a vote is casted. The iris images that receive more than certain number of votes are considered as possible candidates. In order to find the potential matches, the keypoint descriptor of the list of possible candidates is matched with the query iris. Since only small portion of database is scanned to find a match it reduces the query retrieval time and improves accuracy. This approach is tested on UBIRIS, BATH, CASIA and IITK iris databases and shows a substantial improvement over exhaustive search technique in terms of time and accuracy. <s> BIB004 </s> Iris Indexing Techniques: A Review <s> Based on Iris Code <s> The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade. <s> BIB005 </s> Iris Indexing Techniques: A Review <s> Based on Iris Code <s> Iris authentication is one of the most successful applications in video analysis and image processing. In this paper, several novel approaches are proposed to improve the overall performance of iris recognition systems. First, this paper proposes a new eyelash detection algorithm based on directional filters, which achieves a low rate of eyelash misclassification. Second, a multiscale and multidirection data fusion method is introduced to reduce the edge effect of wavelet transformation produced by complex segmentation algorithms. Finally, an iris indexing method on the basis of corner detection is presented to accelerate exhausted the 1: N search in a huge iris database. The performance evaluations are carried out on two popular iris databases, and the test results are experimentally more robust and accurate with less elapsed time compared with most existing methods. <s> BIB006
Iris indexing based on iris code is considered to be a pioneering work on this area proposed in BIB002 . In the above approach three techniques were proposed. The first is based on Principal Component Analysis (PCA) on Iriscode and kmeans algorithm is performed on to reduce the dimension vector. The second is based on the Local Binary Pattern (LBP) in which each image is divided into blocks and a histogram is calculated for all the blocks in the given image. The number of bins in the histogram depends on the radius considered to generate the LBP. The close relationship between LBP histograms is exploited to decrease the number of candidate hypothesis for matching. This technique was analyzed by varying the number of neighborhood pixels for calculating the LBP code. The third technique analyses the inter-pixel relationship of the iris. In order to extract pixel level difference features of blocks, a Signed Pixel Level Difference Histogram (SPLD) is generated. The SPLDH is computed by taking the histogram of the signed differences of the corresponding pixel positions of the two blocks. The entropy of the histogram is calculated in order to determine the importance of the second block with respect to the first block. As the work focus on comparison among the three techniques which results that, it does not perform as well as the indexing technique using block based features of IrisCode, the performance is far better than the indexing technique based on LBP. In spite of the fact that the above work is a revolutionary one for iris indexing technique the hit rate is low and penetration rate it high even for a small-scale database. However it has opened up further research on these indexing techniques. The issue of indexing noisy data has always been challenging. In BIB006 one such approach is attempted. The first step is to detect the eyelash which is misclassified as texture by several algorithms. Hence a method based on the direction of the eyelash was found, which is based on the observation that every eyelash usually grows along a single direction or with a small deflection which can be observed by two directions close to each other. A connection rule is formed in order to reduce the loss as well as detect the eyelash. Based on connection rule, eye lash can be removed according to their position. The second part is feature extraction which is done based on the multiscale and multiorientation data fusion strategy after 2-D Gabor filtering which describes both the scale and direction of the iris texture features. Finally an indexing technique based on the statistical analysis of texture distribution in every block of the normalized iris image is accomplished through Harris corner detection method BIB003 where the unwanted corners are discarded and evenly cropped image are taken. The masked regions of the normalized iris images are marked using ternary indexing codes which guarantees both the accuracy and retrieval. The above method is compared with PCA method BIB002 and SIFT keypoint method BIB004 , has led to the twin advantage of lower penetration rate and miss rate. A geometric hashing method to index a large iris biometric database has been proposed in BIB004 , wherein the input iris image is preprocessed to produce noise independent image using Circular Hough transformation and a sector based approach. Later the local features were extracted by finding the keypoints in the image. These features are invariant to scaling and rotation and to some extent invariant to illumination, viewpoint and occlusion. The SFIT BIB005 is used for detecting keypoints descriptor from the preprocessed image. Further, orientation is approximated for each keypoints and the descriptor vector is defined relative to orientation. The indexing is then done based on detected keypoints. The keypoints are chosen for different combination of basis pair on the assumption that the other point should lie in positive axis. The hash table is formed based on the possible ordered basis pair where the keypoints are scaled to the magnitude of the keypoints equal to 1. Done so, there are a number of entries in the hash table which leads to hash bin occupancy to a non-uniform entries with a peak of accumulated large number of entries. Hence an efficient technique for uniform distribution of entries in the hash table was proposed BIB001 , by which rehashing function was applied to form uniform occupancy. For each entry found in the hash table bin a vote is casted and the votes greater than the threshold were considered to be potential matches and the top is considered as best match. A good accuracy of 98.5 % has been obtained, as geometric hashing is inherently parallel where in the time required includes hashing and again for rehashing 29 s (approximate).