scholarly journals Iris Image Segmentation and Localization using Dynamic Reconfigurable Processor (DRP)

2020 ◽  
Vol 2 (3) ◽  
pp. 147-155
Author(s):  
Smaran S. Rao ◽  
Shreyas R. ◽  
Gajanan Maske ◽  
Antara Roy Choudhury
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Iris Recognition ◽  
Image Capture ◽  
Iris Localization ◽  
Reconfigurable Processor ◽  
Iris Image ◽  
Survey Paper ◽  
Configurable Processor ◽  
Iris Feature Extraction

Recognition of the Iris is among the finest techniques in the field of bio-metrics identification, because the iris has characteristics that are unique and stay the same all through the individual’s life. Iris recognition phases are namely image acquisition, segmentation of iris, localization of iris, feature extraction of iris and matching. This paper, which is an extension of the survey paper Smaran et.al[1], concentrates purely on the procedures of image capture, segmentation as well as localization of the iris. The aim of the paper is to optimize the above mentioned processes in terms of distance of capturing the image, time taken for memory and computation requirements, using the DRP (Dynamic Re-Configurable Processor) technology, uniquely developed by Renesas Electronics (www.renesas.com).

scholarly journals An Improved Bovine Iris Segmentation Method

2019 ◽  
Vol 267 ◽  
pp. 03002
Author(s):  
Zhongliang Luo ◽  
Jingguo Dai ◽  
Yingbiao Jia ◽  
Jiazhong He
Keyword(s):  
Image Segmentation ◽  
Iris Recognition ◽  
Noise Suppression ◽  
Safety Management ◽  
Iris Segmentation ◽  
Iris Image ◽  
Enhancement Method ◽  
Iris Feature Extraction ◽  
Speed And Accuracy ◽  
Image Segmentation Algorithm

In order to improve the performance of bovine iris image segmentation, an improved iris image segmentation algorithm is proposed according to the characteristics of bovine iris image. Firstly, based on mathematical morphology and noise suppression template, the inner and outer edges of bovine iris are detected by dynamic contour tracking and least squares fitting ellipse respectively. Then, the annular iris region is normalized. Finally, the normalized iris image is enhanced with adaptive image enhancement method. The experimental results show that the algorithm can effectively segment iris region, it has good performance of speed and accuracy for iris segmentation, and can eliminate the effects of uneven illumination, iris shrinkage and rotation, it promotes iris feature extraction and matching, which has certain reference significance for iris recognition research and meat food safety management of large livestock.

Iris Feature Extraction and Matching Based on Space Pyramid

2012 ◽  
Vol 459 ◽  
pp. 7-11
Author(s):  
Wei Peng ◽  
Qun Ying Yang ◽  
Song Gu
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Recognition System ◽  
Classifier Design ◽  
Effectiveness And Efficiency ◽  
System Calculation ◽  
Pyramid Matching ◽  
Key Steps ◽  
Iris Feature Extraction ◽  
Match Effect

Iris feature extraction and classifier design are the key steps in iris recognition system, because it will directly affect the effect of iris recognition, especially for real-time iris recognition system, in order to extract enough information as possible while reducing the system Calculation, and find a balance between effectiveness and efficiency in the identification. the current proposed Gobor Dougman filtering is the main feature extraction. However, in the kinds of embedded systems, Gobor function need large amount of calculation, so this chapter put the pyramid matching classification in scenes into feature extraction, established a new model of feature extraction and recognition. The match effect is ideal in after large number of tests

scholarly journals Eyelids, eyelashes detection algorithm and houghtransform method for noise removal in iris recognition

2020 ◽  
Vol 18 (2) ◽  
pp. 731
Author(s):  
Bounegta Nadia ◽  
Bassou Abdessalam ◽  
Beladgham Mohamed
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Iris Recognition ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Detection Technique ◽  
Biometric System ◽  
Transform Method ◽  
Iris Image ◽  
Identical Feature

<p><span>The biometric system is based on human’s behavioral and physical characteristics. Among all of these, iris has unique structure, higher accuracy and it can remain stable over a person’s life. Iris recognition is the method by which system recognize a person by their unique identical feature found in the iris. Iris recognition technology includes four subsections as, capturing of the iris image, segmentation, extraction of the needed features and matching. This paper is a detail description of eyelids; eyelashes detection technique and Hough transform method applied on iris image. </span></p>

A Two-Step Iris Recognition Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 559-562
Author(s):  
Jin Liu ◽  
Cang Ming Liu ◽  
Ting Ting Liu
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Feature Recognition ◽  
Recognition Algorithm ◽  
Single Type ◽  
Gabor Transform ◽  
Single Feature ◽  
Traditional Algorithm ◽  
And Performance ◽  
Iris Feature Extraction

Starting from the goal to overcome the limitation of the single type algorithm of iris recognition. In this paper, a two-step iris recognition algorithm is proposed based on feature extraction on corner and Gabor transform .First of all, it uses the corner feature of iris texture to recognize for achieving the purpose of removal of alien and confirm the vast majority of similar samples rapidly; if it unabes to determine the category in the first step, the result of the first step is used as the initial angle in normalized phase, and then it carries out iris feature extraction and recognition based on Gabor transform. The experimental results show that this results the speed and performance in the recognition improved significantly than the traditional algorithm of single feature recognition.

A Robust Iris Feature Extraction Approach Based on Monogenic and 2D Log-Gabor Filters

2015 ◽  
Vol 24 (2) ◽  
pp. 161-179 ◽  
Author(s):  
Walid Aydi ◽  
Nade Fadhel ◽  
Nouri Masmoudi ◽  
Lotfi Kamoun
Keyword(s):  
Feature Extraction ◽  
High Performance ◽  
Iris Recognition ◽  
Nearest Neighbor ◽  
Hamming Distance ◽  
Extraction Process ◽  
Distance Measures ◽  
Computational Time ◽  
Fitting Method ◽  
Iris Feature Extraction

AbstractThis article suggests an enhancement of the Masek circle model approach usually used to find a trade-off between modeling complexity, algorithm accuracy, and computational time, mainly for embedded systems where the real-time aspect is a high challenge. Moreover, most commercialized systems (Aoptix, Mkc-series, IriScan, etc.) today frame iris regions by circles. This work led to several novelties: first, in the segmentation process, the corneal reflection removal method based on morphological reconstruction and pixel connectivity was implemented. Second, the picture size reduction was applied according to nearest-neighbor interpolation. Third, the image gradient of the convolved-reduced picture was then generated using four proposed matrices. Fourth, and to reduce the complexity of the traditional method for the detection of the top and lower eyelids, a new method based on the Radon transform and the least squares fitting method was applied. Fifth, eyelashes were detected via the diagonal gradient and thresholding method. Monogenic signal was used in the feature extraction process. Finally, two distance measures were selected as a metric for recognition. Our experimental results using CASIA iris database V3.0 reveal that the proposed method provides a high performance in terms of speed and accuracy. Using dissimilarity modified Hamming distance, the accuracy of iris recognition was improved, with a false acceptance rate equal to 3% and a speed at least eight times as compared with the state of the art.

Iris Boundary Localization Based on Improve Hough Transform

2013 ◽  
Vol 760-762 ◽  
pp. 1576-1580
Author(s):  
Guo Yu Zhang ◽  
Hui Zhao ◽  
Min Han ◽  
Li Ling Chen
Keyword(s):  
Image Segmentation ◽  
Hough Transform ◽  
Traditional Method ◽  
Iris Recognition ◽  
Recognition System ◽  
Low Contrast ◽  
Exterior Boundary ◽  
Iris Image ◽  
Interior Boundary ◽  
Key Steps

ris location is one of the key steps of iris recognition system. Non-ideal iris image has some problems, such as eyelid and eyelash occlusion, low contrast of iris and sclera, uneven illumination, and so on. Because of that, its difficult to identify the boundary, especially the exterior boundary. Therefore, this paper proposes a method based on the improved Hough Transform. First, use the minimum method to find the datum point in the pupil, after that identify the valid area of the interior boundary base on that point. Apply the improved Hough Transform to that valid area to identify the interior boundary of the iris image. Then regard the center of the interior circle as our new datum point, use the same method to identify the exterior boundary. Experiment results show that our algorithm has higher accuracy than traditional method on the non-ideal iris image segmentation.

scholarly journals FEATURE VECTOR CONSTRUCTION METHOD FOR IRIS RECOGNITION

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 233-236
Author(s):  
G. Odinokikh ◽  
A. Fartukov ◽  
M. Korobkin ◽  
J. Yoo
Keyword(s):  
Feature Extraction ◽  
Iris Recognition ◽  
Feature Vector ◽  
Extraction Methods ◽  
Gabor Filtering ◽  
Prior Art ◽  
Vector Construction ◽  
Novel Approach ◽  
Iris Feature Extraction ◽  
Subsequent Comparison

One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.

Analysis of Iris Images Segmentation Methods Based on Level Set

2013 ◽  
Vol 753-755 ◽  
pp. 2985-2989
Author(s):  
Ying Chen ◽  
Feng Yu Yang
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Iris Recognition ◽  
Evaluation Criteria ◽  
Computation Time ◽  
Personal Identification ◽  
Experimental Results ◽  
Iris Localization ◽  
Segmentation Accuracy ◽  
Pass Through

Iris recognition plays an important role in personal identification. In this study, we utilized CREASEG experimental platform to analyze the performance of some state-of-the-art image segmentation algorithms based on level set. Performance evaluation criteria include segmentation accuracy and computation time of pupil and iris localization. Four iris images were taken as experimental samples. The experimental results on those image samples demonstrate that Chan-Vese model achieve the best performance among all six algorithms. Furthermore, experimental results also show that energy functions play an important role, which should not make evolution curve to terminate at local minima or pass through the boundary. This study can provide certain referential significance in how to select image segmentation algorithm based on level set.

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