A New Segmentation Method for Iris Recognition Using the Complex Inversion Map and Best-Fitting Curve

2008 ◽  
pp. 364-371
Author(s):  
Sepehr Attarchi ◽  
Karim Faez ◽  
Mir Hashem Mousavi
Keyword(s):  
Iris Recognition ◽  
Segmentation Method ◽  
Best Fitting

scholarly journals Iris Segmentation

2018 ◽  
Vol 7 (2.5) ◽  
pp. 77
Author(s):  
Anis Farihan Mat Raffei ◽  
Rohayanti Hassan ◽  
Shahreen Kasim ◽  
Hishamudin Asmuni ◽  
Asraful Syifaa’ Ahmad ◽  
...  
Keyword(s):  
Iris Recognition ◽  
Image Data ◽  
Recognition System ◽  
Environmental Condition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Transform Method ◽  
Circular Hough Transform ◽  
Segmentation Methods

The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection.  

Detecting Eyelash and Reflection for Accurate Iris Segmentation

2003 ◽  
Vol 17 (06) ◽  
pp. 1025-1034 ◽  
Author(s):  
Wai-Kin Kong ◽  
David Zhang
Keyword(s):  
Statistical Model ◽  
Iris Recognition ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Edge Detector ◽  
Recognition Error ◽  
Weak Reflection

Accurate iris segmentation is presented in this paper, which is composed of two parts, reflection detection and eyelash detection. Eyelashes are classified into two categories, separable and multiple. An edge detector is applied to detect separable eyelashes, and intensity variances are used to recognize multiple eyelashes. Reflection is also divided into two types, strong and weak. A threshold and statistical model is proposed to recognize the strong and weak reflection, respectively. We have developed an iris recognition approach for testing the effectiveness of the proposed segmentation method. The results show that the proposed method can reduce recognition error for the iris recognition approach.

Iris Segmentation for Non-ideal Images

2015 ◽  
Vol 74 (3) ◽  
Author(s):  
Nasharuddin Zainal ◽  
Abduljalil Radman ◽  
Mahamod Ismail ◽  
Md Jan Nordin
Keyword(s):  
Iris Recognition ◽  
Recognition Algorithm ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
West Virginia University ◽  
Segmentation Methods ◽  
Biometric Systems ◽  
Segmentation Algorithms ◽  
Recognition Systems ◽  
Constrained Conditions

Iris recognition has been regarded as one of the most reliable biometric systems over the past years. Previous studies have shown that the performance of iris recognition systems highly dependent on the performance of their segmentation algorithms. Iris segmentation is the process to isolate the iris region from the surrounded structures of the eye image. However, several iris segmentation algorithms have been developed in the literature, but their segmentation and recognition accuracies drastically degrade with non-ideal iris images acquired in less constrained conditions. Thus, it is crucial to develop a new iris segmentation method to improve iris recognition using non-ideal images. Hence, the objective of this paper is an iris segmentation method on the basis of optimization to isolate the iris region from non-ideal iris images such those affected by reflections, blurred boundaries, eyelids occlusion, and gaze-deviation. Experimental results on the off axis/angle West Virginia University (WVU) iris database demonstrated the superiority of the developed method over state-of-the-art iris segmentation methods considered in this paper. The performance of an iris recognition algorithm based on the developed iris segmentation method was observed to be improved.  

An Automated Segmentation Method For Iris Recognition

2006 ◽  
Author(s):  
Sandipan P Narote ◽  
Abhilasha S Narote ◽  
Laxman M Waghmare ◽  
Arun N Gaikwad
Keyword(s):  
Iris Recognition ◽  
Automated Segmentation ◽  
Segmentation Method

Study on Iris Segmentation Method Using Chan-Vese Model

2013 ◽  
Vol 753-755 ◽  
pp. 2995-2999
Author(s):  
Ying Chen ◽  
Feng Yu Yang
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Iris Localization ◽  
Zero Crossing ◽  
Critical Step ◽  
Iris Image ◽  
Pros And Cons

The first and critical step in the process of an iris recognition system is iris segmentation. Firstly, we detailedly describe the process of pupil and iris localization based on Chan-Vese model. Secondly, we describe the process of unwrapping iris annule region, and obtain rectangular image with the same width but different height. Thirdly, cut rectangular iris image to get normalized image. Fourthly, Multi-channel 2D Gabor, 1-D wavelets and zero-crossing methods were used to extract feature; consequently, decidability indexes of intra-class and inter-class were obtained. Finally, comparatively analyze the pros and cons of the proposed method. Three public iris images databases were taken as experimental samples, the experimental results on these image samples demonstrate that the proposed algorithm has certain advantage.

An Efficient Iris Segmentation Method for Non-Ideal Iris Images

2013 ◽  
Vol 658 ◽  
pp. 597-601
Author(s):  
Xiao Wen Xu
Keyword(s):  
Level Set ◽  
Iris Recognition ◽  
Outer Boundary ◽  
Least Square ◽  
Projection Algorithm ◽  
Segmentation Method ◽  
Iris Segmentation ◽  
Fitting Algorithm ◽  
Segmentation Accuracy ◽  
The Impact

Segmenting the non-ideal iris images accurately is a main problem for iris recognition, due to the impact of the eyelids, eyelashes and deformation. The paper presents an iris segmentation method based on an improved level set. Firstly, we used gray projection algorithm to locate the pupil. Secondly, we applied the least square fitting algorithm to estimate the boundary between the pupil and the iris. Finally, we used the level set method to accurately segment the iris. Experimental results demonstrate the segmentation accuracy for outer boundary of the iris is 98.59%. The method presented in this paper is superior to Daugman method and Hough transform algorithm in iris segmentation, especially for non-ideal iris images.

scholarly journals Entropy Based CNN for Segmentation of Noisy Color Eye Images using Color, Texture and Brightness Contour Features

2019 ◽  
Vol 8 (2) ◽  
pp. 2116-2124
Keyword(s):  
Neural Network ◽  
Iris Recognition ◽  
Image Database ◽  
Head Position ◽  
Segmentation Algorithm ◽  
Content Based Image Retrieval ◽  
Segmentation Method ◽  
Segmentation Accuracy ◽  
Recognition Systems ◽  
Color Texture

Today’s most of the iris recognition systems are strongly dependent on user’s cooperation during image acquisition such as stop-stair condition, head position and camera distance. Images are taken in NIR spectrum to reduce the noise such as effect of illumination. Challenges faced by existing iris recognition systems are such as they are time consuming due to need of extra hardware setup and unable to achieve better performance for images acquired on-the-move, at-a-distance, etc.. To overcome these challenges, in this paper we proposed novel segmentation algorithm based on content based image retrieval approach. In proposed segmentation method, color, texture and brightness contour features were extracted. Entropy value for these extracted contour features was measured to reduce the dimensionality of features. These set of calculated entropy value was given as input to convolutional neural network to cluster noisy eye image into iris, sclera and pupil region. The proposed segmentation algorithm was experimented on freely available UBIRIS.V2 noisy eye image database using MATLAB. The experimentation results shows that proposed segmentation method is superior as compared to existing method by reducing average segmentation time up to 0.9sec and increasing segmentation accuracy up to 97% for non ideal color eye images.

Preliminary Evaluation of a Weibull Function for Fitting Slow-Component Eye Velocity over the Time Course of Caloric-Induced Nystagmus

1989 ◽  
Vol 32 (3) ◽  
pp. 681-687 ◽  
Author(s):  
C. Formby ◽  
B. Albritton ◽  
I. M. Rivera
Keyword(s):  
Time Course ◽  
Slow Component ◽  
Weibull Function ◽  
Preliminary Evaluation ◽  
Mathematical Function ◽  
Before And After ◽  
Best Fitting ◽  
Eye Velocity ◽  
Fitting Parameters ◽  
Weibull Equation

We describe preliminary attempts to fit a mathematical function to the slow-component eye velocity (SCV) over the time course of caloric-induced nystagmus. Initially, we consider a Weibull equation with three parameters. These parameters are estimated by a least-squares procedure to fit digitized SCV data. We present examples of SCV data and fitted curves to show how adjustments in the parameters of the model affect the fitted curve. The best fitting parameters are presented for curves fit to 120 warm caloric responses. The fitting parameters and the efficacy of the fitted curves are compared before and after the SCV data were smoothed to reduce response variability. We also consider a more flexible four-parameter Weibull equation that, for 98% of the smoothed caloric responses, yields fits that describe the data more precisely than a line through the mean. Finally, we consider advantages and problems in fitting the Weibull function to caloric data.

Sign in / Sign up

Export Citation Format

Share Document