Book ChapterDOI
Iris Based Human Verification Algorithms
TLDR
In this paper three algorithms for iris verification have been presented and the experimental results show that the algorithm based on Circular - Mellin Transform gives the best result with an accuracy of 95.45%.Abstract:
In this paper three algorithms for iris verification have been presented. Iris detection algorithms include the normalization and iris extraction steps. Three algorithms for verification process are (a) Algorithm using radial and circular features, (b) Algorithm using Fourier transforms and (c) Algorithm using Circular-Mellin transforms. Proposed algorithms have been tested on CASIA database and some non-infrared Iris images. The experimental results show that the algorithm based on Circular - Mellin Transform gives the best result with an accuracy of 95.45%. Some initial experiments on non-infrared iris images shows that this algorithm can work on such images but it still requires some more attention and this is our future work.read more
Citations
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Journal ArticleDOI
Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing
TL;DR: 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.
Journal Article
Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological Features
TL;DR: A novel iris recognition system using 1D log polar Gabor wavelet and Euler numbers and the proposed decision strategy uses these features to authenticate an individual's identity while maintaining a low false rejection rate.
Journal ArticleDOI
Robust memory-efficient data level information fusion of multi-modal biometric images
TL;DR: A novel multi-level wavelet based fusion algorithm that combines information from fingerprint, face, iris, and signature images of an individual into a single composite image that reduces the memory size, increases the recognition accuracy using multi-modal biometric features, and withstands common attacks.
Proceedings ArticleDOI
A review of issues and challenges in designing Iris recognition Systems for noisy imaging environment
Kamal Hajari,Kishor K. Bhoyar +1 more
TL;DR: Different challenges in designing iris recognition systems for noisy imaging environment are reviewed and methodologies involved in overcoming these issues are discussed and some measures to improve the accuracy of such systems are suggested.
Journal Article
IRIS Recognition Using Neural Network
TL;DR: The experimental result gives the comparison between different methods and shows that neural network is a promising and effective approach in iris recognition.
References
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A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI
High confidence visual recognition of persons by a test of statistical independence
TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
Proceedings ArticleDOI
Texture segmentation using circular-Mellin operators
G. Ravichandran,Mohan M. Trivedi +1 more
TL;DR: This paper discusses the use of circular-Mellin features for segmenting an image into homogenous regions and notes that while both these feature extractors have similar functional form to the Gabor functions, the distortion-invariant characteristics of the circular-mellin operators make them preferable for texture segmentation.