Abstract—Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2 255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature. Index Terms—Biometrics, iris recognition, local sharp variations, personal identification, transient signal analysis, wavelet transform. I.

With an increasing emphasis on security, automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications. In general, a typical iris recognition system includes iris imaging, iris liveness detection, and recognition. This paper focuses on the last issue and describes a new scheme for iris recognition from an image sequence. We first assess the quality of each image in the input sequence and select a clear iris image from such a sequence for subsequent recognition. A bank of spatial filters, whose kernels are suitable for iris recognition, is then used to capture local characteristics of the iris so as to produce discriminating texture features. Experimental results show that the proposed method has an encouraging performance. In particular, a comparative study of existing methods for iris recognition is conducted on an iris image database including 2,255 sequences from 213 subjects. Conclusions based on such a comparison using a nonparametric statistical method (the bootstrap) provide useful information for further research.

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A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. However, published results have usually been produced under favourable conditions, and there have been no independent trials of the technology. The work presented in this thesis involved developing an ‘open-source ’ iris recognition system in order to verify both the uniqueness of the human iris and also its performance as a biometric. For determining the recognition performance of the system two databases of digitised greyscale eye images were used. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localise the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then

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In this paper, efficient biometric security techniques for iris recognition system with high performance and high confidence are described. The system is based on an empirical analysis of the iris image and it is split in several steps using local image properties. The system steps are capturing iris patterns; determine the location of the iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting the iris code based on texture analysis using wavelet transforms; and classification of the iris code. The proposed system use the wavelet transforms for texture analysis, and it depends heavily on knowledge of the general structure of a human iris.

Iris recognition is the most accurate biometrics which has received increasing attention in departments which require high security. In this paper, we discussed Gabor Wavelet, Local Binary Pattern, Histogram of Oriented Gradient techniques to extract features on specific portion of the iris for improving the performance of an iris recognition system. The main aim of this paper is to show that is enough to choose the half portion of the iris to recognize authentic users and to reject imposters instead of whole extension of the iris. The proposed methods are evaluated based upon False Rejection Rate (FRR) and False Acceptance Rate (FAR) and the experimental results show that this technique produces good performance on MMU iris database.

Abstract—One of the basic challenges to robust iris recognition is iris segmentation. This paper proposes the use of a feature saliency algorithm and an artificial neural network to perform iris segmentation. Many current Iris segmentation approaches assume a circular shape for the iris boundary if the iris is directly facing the camera. Occlusion by the eyelid can cause the visible boundary to have an irregular shape. In our approach an artificial neural network is used to statistically classify each pixel of an iris image with no assumption of circularity. First, a feed-forward feature saliency technique is performed to determine which combination of features contains the greatest discriminatory information. Image brightness, local moments, local orientated energy measurements and relative pixel location are evaluated for saliency. Next, the set of salient features is used as the input to a multi-layer perceptron feed-forward artificial neural network trained for classification. Testing showed 96.46 percent accuracy in determining which pixels in an image of the eye were iris pixels. For occluded images, the iris masks created by the neural network were consistently more accurate than the truth mask created using the circular iris boundary assumption. Post-processing to retain the largest contiguous piece in the iris mask increased the accuracy to 98.2 percent. I.

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