A Novel Approach for Iris Recognition in Unconstrained Environment

2014 ◽  
Vol 6 (2) ◽  
Author(s):  
Navjot Kaur ◽  
Mamta Juneja
Keyword(s):  
Iris Recognition ◽  
Novel Approach

A novel approach for iris recognition

2016 ◽  
Author(s):  
Rocky Yefrenes Dillak ◽  
Martini Ganantowe Bintiri
Keyword(s):  
Iris Recognition ◽  
Novel Approach

An efficient novel approach for iris recognition based on stylometric features and machine learning techniques

2020 ◽  
Vol 107 ◽  
pp. 144-157 ◽  
Author(s):  
Saša Adamović ◽  
Vladislav Miškovic ◽  
Nemanja Maček ◽  
Milan Milosavljević ◽  
Marko Šarac ◽  
...  
Keyword(s):  
Machine Learning ◽  
Iris Recognition ◽  
Machine Learning Techniques ◽  
Novel Approach ◽  
Learning Techniques

Non-cooperative Iris Segmentation: A Two-stage Approach

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Rajeev Kumar ◽  
K. Arthi
Keyword(s):  
Level Set ◽  
Active Contour ◽  
Iris Recognition ◽  
Pso Algorithm ◽  
Recognition System ◽  
Iris Segmentation ◽  
Two Stage ◽  
Geodesic Active Contour ◽  
Level Set Segmentation ◽  
Novel Approach

: Recently, segmentation of iris image is the most important process in a robust iris recognition system due to the images captured from non-cooperative environments which introduce occlusions, blur, specular reflections, and off-axis. However, several techniques are developed to overcome these drawbacks in the iris segmentation process; it is still a challenging task to localize the iris texture regions. In this research, an effective two-stage of iris segmentation technique is proposed in a non-cooperative environment. At first, modified Geodesic Active Contour-based level set segmentation with Particle Swarm Optimization (PSO) is employed for iris segmentation. In this, the PSO algorithm is used to minimize the energy of the gradient descent equation in a region-based level set segmentation algorithm. Then, the global threshold-based segmentation is employed for pupil region segmentation. The experiment considered two well-known databases such as UBIRIS.V1 and UBIRIS.V2. The simulation outcomes demonstrate that the proposed novel approach attained more accurate and robust iris segmentation under non-cooperative conditions. Also, the results of the modified Geodesic Active Contour-based level set segmentation with the PSO algorithm attained better results than the conventional segmentation techniques.

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.

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