Measuring the Quality of IRIS Segmentation for Improved IRIS Recognition Performance

2012 ◽  
Author(s):  
R. Hentati ◽  
B. Dorizzi ◽  
Y. Aoudni ◽  
M. Abid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Iris Segmentation

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.  

scholarly journals Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1308 ◽  
Author(s):  
Mohsen Jenadeleh ◽  
Marius Pedersen ◽  
Dietmar Saupe
Keyword(s):  
Image Quality ◽  
Visible Light ◽  
Iris Recognition ◽  
Recognition Performance ◽  
Rejection Rate ◽  
Recognition System ◽  
Poor Quality ◽  
Iris Image ◽  
Recognition Systems

Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

An iris segmentation algorithm based on edge orientation for off-angle iris recognition

2013 ◽  
Author(s):  
Mahmut Karakaya ◽  
Del Barstow ◽  
Hector Santos-Villalobos ◽  
Christopher Boehnen
Keyword(s):  
Iris Recognition ◽  
Segmentation Algorithm ◽  
Iris Segmentation ◽  
Edge Orientation

scholarly journals FMnet: Iris Segmentation and Recognition by Using Fully and Multi-Scale CNN for Biometric Security

2019 ◽  
Vol 9 (10) ◽  
pp. 2042 ◽  
Author(s):  
Rachida Tobji ◽  
Wu Di ◽  
Naeem Ayoub
Keyword(s):  
Neural Networks ◽  
Iris Recognition ◽  
State Of The Art ◽  
Features Extraction ◽  
Iris Segmentation ◽  
Recognition Method ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Biometric Security

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

scholarly journals Optical Flow Filtering-Based Micro-Expression Recognition Method

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2056
Author(s):  
Junjie Wu ◽  
Jianfeng Xu ◽  
Deyu Lin ◽  
Min Tu
Keyword(s):  
Optical Flow ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Recognition Method ◽  
Video Clips ◽  
Low Degree ◽  
Micro Expression

The recognition accuracy of micro-expressions in the field of facial expressions is still understudied, as current research methods mainly focus on feature extraction and classification. Based on optical flow and decision thinking theory, we propose a novel micro-expression recognition method, which can filter low-quality micro-expression video clips. Determined by preset thresholds, we develop two optical flow filtering mechanisms: one based on two-branch decisions (OFF2BD) and the other based on three-way decisions (OFF3WD). In OFF2BD, which use the classical binary logic to classify images, and divide the images into positive or negative domain for further filtering. Differ from the OFF2BD, OFF3WD added boundary domain to delay to judge the motion quality of the images. In this way, the video clips with low degree of morphological change can be eliminated, so as to directly improve the quality of micro-expression features and recognition rate. From the experimental results, we verify the recognition accuracy of 61.57%, and 65.41% for CASMEII, and SMIC datasets, respectively. Through the comparative analysis, it shows that the scheme can effectively improve the recognition performance.

scholarly journals Noncircular Iris Segmentation Based on Weighted Adaptive Hough Transform Using Smartphone Database

2018 ◽  
Vol 15 (2) ◽  
pp. 739-743 ◽  
Author(s):  
Noor Amjed ◽  
Fatimah Khalid ◽  
Rahmita Wirza O. K. Rahmat ◽  
Hizmawati Binit Madzin
Keyword(s):  
Hough Transform ◽  
Iris Recognition ◽  
Recognition System ◽  
Iris Segmentation ◽  
Negative Image ◽  
Processing Step ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
High Level ◽  
Imaging Conditions

Iris segmentation methods work based on ideal imaging conditions which produce good output results. However, the segmentation accuracy of an iris recognition system significantly influences its performance, especially with data that captured in unconstrained environment of the Smartphone. This paper proposes a novel segmentation method for unconstrained environment of the Smartphone videos based on choose the best frames from the videos and try to enhance the contrast of this frames by applying the two fuzzy logic membership functions on the negative image which delimit between dark and bright regions in able to make the dark region darker and the bright region brighter. This pre-processing step Facilitates the work of the Weighted Adaptive Hough Transform to automatically find the diameter of the iris region to apply the osiris v4.1. The proposed method results on the video of (Mobile Iris Challenge Evaluation (MICHE))-I, iris databases indicate a high level of accuracy and more efficient computationally using the proposed technique.

IMPROVEMENT OF IRIS RECOGNITION PERFORMANCE USING REGION-BASED ACTIVE CONTOURS, GENETIC ALGORITHMS AND SVMs

2010 ◽  
Vol 24 (08) ◽  
pp. 1209-1236 ◽  
Author(s):  
KAUSHIK ROY ◽  
PRABIR BHATTACHARYA
Keyword(s):  
Genetic Algorithms ◽  
Iris Recognition ◽  
Active Contours ◽  
Active Contour Model ◽  
Recognition Performance ◽  
Texture Features ◽  
Motion Blur ◽  
Misclassification Error ◽  
Support Vector ◽  
Iris Image

Most existing iris recognition algorithms focus on the processing and recognition of the ideal iris images that are acquired in a controlled environment. In this paper, we process the nonideal iris images that are captured in an unconstrained situation and are affected severely by gaze deviation, eyelids and eyelashes occlusions, nonuniform intensity, motion blur, reflections, etc. The proposed iris recognition algorithm has three novelties as compared to the previous works; firstly, we deploy a region-based active contour model to segment a nonideal iris image with intensity inhomogeneity; secondly, genetic algorithms (GAs) are deployed to select the subset of informative texture features without compromising the recognition accuracy; Thirdly, to speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The verification and identification performance of the proposed scheme is validated on three challenging iris image datasets, namely, the ICE 2005, the WVU Nonideal, and the UBIRIS Version 1.

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