Proceedings ArticleDOI
Biometric personal identification based on iris patterns
Zhu Yong,Tieniu Tan,Yunhong Wang +2 more
- Vol. 2, pp 2801-2804
TLDR
A new system for personal identification based on iris patterns is presented, which employs the rich 2D information of the iris and is translation, rotation, and scale invariant.Abstract:
A new system for personal identification based on iris patterns is presented in this paper. It is composed of iris image acquisition, image preprocessing, feature extraction and classifier design. The algorithm for iris feature extraction is based on texture analysis using multichannel Gabor filtering and wavelet transform. Compared with existing methods, our method employs the rich 2D information of the iris and is translation, rotation, and scale invariant.read more
Citations
More filters
Journal ArticleDOI
Personal identification based on iris texture analysis
TL;DR: A bank of spatial filters, whose kernels are suitable for iris recognition, is used to capture local characteristics of the iris so as to produce discriminating texture features and results show that the proposed method has an encouraging performance.
Journal ArticleDOI
Efficient iris recognition by characterizing key local variations
TL;DR: 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.
Recognition of Human Iris Patterns for Biometric Identification
TL;DR: 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.
Person Identification Technique Using Human Iris Recognition
TL;DR: A new iris recognition system that implements (i) gradient decomposed Hough transform / integro-differential operators combination for iris localization and (ii) the "analytic image" concept (2D Hilbert transform) to extract pertinent information from iris texture is examined.
Proceedings ArticleDOI
Iris recognition using circular symmetric filters
Li Ma,Yunhong Wang,Tieniu Tan +2 more
TL;DR: The method consists of three major components: image preprocessing, feature extraction and classifier design, which uses an efficient approach called nearest feature line (NFL) for iris matching.
References
More filters
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.
Journal ArticleDOI
Iris recognition: an emerging biometric technology
TL;DR: This paper examines automated iris recognition as a biometrically based technology for personal identification and verification from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment.
Journal ArticleDOI
A human identification technique using images of the iris and wavelet transform
Wageeh Boles,Boualem Boashash +1 more
TL;DR: A new approach for recognizing the iris of the human eye is presented, and the resulting one-dimensional signals are compared with model features using different dissimilarity functions.
Journal Article
Texture Classification by Wavelet Packet Signatures.
Andrew F. Laine,Jian Fan +1 more
TL;DR: In this article, the performance of wavelet packet spaces is measured in terms of sensitivity and selectivity for the classification of twenty-five natural textures, where both energy and entropy metrics are computed for each wavelet packets and incorporated into distinct scale space representations, where each texture channel reflected a specific scale and orientation sensitivity.
Journal ArticleDOI
Texture classification by wavelet packet signatures
Andrew F. Laine,Jian Fan +1 more
TL;DR: The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture.