Iris Indexing Techniques: A Review <s> Based on Iris Color <s> In a very large iris database, the design of an effective iris indexing method reduces the computational complexity of feature matching process. This paper presents a new iris indexing technique based on the iris color for noisy iris images. The proposed method using the chrominance components achieves high indexing performance in defocused, reflection-contained and eyelid-occluded iris images. The performance measures such as the hit rate and the penetration rate computed on a recently created noisy iris database, namely UBIRIS (Proenca and Alexandre, 2005), show the effectiveness of the iris color to index very large iris databases. <s> BIB001 </s> Iris Indexing Techniques: A Review <s> Based on Iris Color <s> This paper proposes an efficient indexing scheme that can be used for retrieval from a large iris database. For a given color iris query image, the proposed indexing scheme makes use of iris color to determine an index and uses this index to reduce the search space in the large iris database. Further, for query q, the retrieval technique uses iris texture to find the top best match from the reduced search space. The proposed technique has been tested on two publicly available color iris databases, viz UPOL [10] of 384 images and UBIRIS [13] of 1860 fully noisy images and is found to be robust against change in gaze, illumination, partial occlusions and scale. In both the databases, the test reveals that a small subspace is sufficient to achieve 100% hitrate for the top best match under various scales, illumination and partial occlusion. The performance of the proposed indexing scheme is analyzed against the group based color indexing scheme proposed in [14]. The results show that proposed indexing scheme is performing better as compared to group based color indexing scheme with respect to hitrate, penetration rate and CMC curve. <s> BIB002
A novel indexing method based on natural iris color was proposed BIB001 . The input image is segmented based on Hough Circular transform to recognize the boundaries. The input image which was then converted from RGB space to YCbCr color space and partition is done based on the blue and red color indices. Depending on the intensity of red and blue color the bins are selected and stored into their respective bins. At the time of retrieval based on the color indices the bin is selected and the nearest identities are selected based on the set intersection between the bins. The system performs well for a small subset since there can be small intra-class variation which may lead to correct grouping. But for high degree of performance, the inter-class variation should be high ie database should be grouped with a large number of subsets, which again affects the search time and efficiency. An indexing scheme which makes uses of iris color to determine an index and then use the index to reduce the search space in large iris databases was proposed in BIB002 . The input is a color iris image, which is originally in RGB color space, is converted to chrominance of blue and red color YCbCr. Since YCbCr color space allows the color indices to operate well in illumination a high indexing performance can be obtained. To identify an iris based on the color they had used color histogram was used, where each element contains the number of pixel having the color in the image and consider the pdf (probability density function) of the color value. Based on the color distribution, the image is distributed in different bins of the histogram and during matching its nearest neighbor was found by comparing all the bins in the database using K-NN algorithm. Using kd-tree identification and retrieval, the top best match from the subset was done. The system is robust and yields a good hit rate but the time required for a top best match is O(log N+k) where k is size of subset, and on average it needs to traverse at least one leaf node of the tree. A novel iris indexing approach on noisy dataset and color as the indexing factor was proposed  . The noisy iris image is located for the region of interest (ROI) based on Adaboost method and the eyelids, eyelash spectaculars refection were removed from the image. After determining the ROI the iris color image was converted to RGB color space and classified accordingly. The average color value is normalized so as to keep away from bad consequence caused by illumination. The indexing is based on the color components with their internal length space interval between the color components. The complexity in this technique is that many of the coarse classification are not correctly classified and heterogeneous lighting condition influence the color distribution. Hence there is a need for color distribution computation which corrects the color based on standard calibration target method. The result seems to be more sensible that both of the normalized average values of each color component are close each other. The hit rate and penetration rate were computed before and after color correction and observably the HR after color correction is much higher than without color correction. The system after color calibration with small noisy dataset works well but when the size grows classification again can become a problem.