Journal ArticleDOI
Efficient iris recognition by characterizing key local variations
Reads0
Chats0
TLDR
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.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.read more
Citations
More filters
Journal ArticleDOI
Image understanding for iris biometrics: A survey
TL;DR: This survey covers the historical development and current state of the art in image understanding for iris biometrics and suggests a short list of recommended readings for someone new to the field to quickly grasp the big picture of irisBiometrics.
Book ChapterDOI
UBIRIS: a noisy iris image database
Hugo Proença,Luís A. Alexandre +1 more
TL;DR: A new iris database that contains images with noise is presented, in contrast with the existing databases, that are noise free.
Journal ArticleDOI
DCT-Based Iris Recognition
TL;DR: A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical equal error rate (EER) is predicted to be as low as 2.59 times 10-1 available data sets.
Journal ArticleDOI
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
TL;DR: Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.
Journal ArticleDOI
A review of biometric technology along with trends and prospects
TL;DR: An extensive review of biometric technology is presented here, focusing on mono-modal biometric systems along with their architecture and information fusion levels.
References
More filters
Journal ArticleDOI
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.
Book
A wavelet tour of signal processing
TL;DR: An introduction to a Transient World and an Approximation Tour of Wavelet Packet and Local Cosine Bases.
Journal ArticleDOI
Generalizing the hough transform to detect arbitrary shapes
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.
Journal ArticleDOI
Singularity detection and processing with wavelets
Stéphane Mallat,Wen-Liang Hwang +1 more
TL;DR: It is proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents.
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.