Iris Segmentation Based on Black Hole Algorithm for Biometric System

Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.

Download Full-text

A Novel Iris Segmentation Scheme

Mathematical Problems in Engineering ◽

10.1155/2014/684212 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 3

Author(s):

Chen-Chung Liu ◽

Pei-Chung Chung ◽

Chia-Ming Lyu ◽

Jui Liu ◽

Shyr-Shen Yu

Keyword(s):

Hough Transform ◽

Iris Recognition ◽

Image Database ◽

Recognition System ◽

Experimental Results ◽

Circle Fitting ◽

Iris Segmentation ◽

Iris Image ◽

Conventional Methods ◽

Key Steps

One of the key steps in the iris recognition system is the accurate iris segmentation from its surrounding noises including pupil, sclera, eyelashes, and eyebrows of a captured eye-image. This paper presents a novel iris segmentation scheme which utilizes the orientation matching transform to outline the outer and inner iris boundaries initially. It then employs Delogne-Kåsa circle fitting (instead of the traditional Hough transform) to further eliminate the outlier points to extract a more precise iris area from an eye-image. In the extracted iris region, the proposed scheme further utilizes the differences in the intensity and positional characteristics of the iris, eyelid, and eyelashes to detect and delete these noises. The scheme is then applied on iris image database, UBIRIS.v1. The experimental results show that the presented scheme provides a more effective and efficient iris segmentation than other conventional methods.

Download Full-text

Optimization of Consignment-Store-Based Supply Chain with Black Hole Algorithm

Complexity ◽

10.1155/2017/6038973 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 12

Author(s):

Ágota Bányai ◽

Tamás Bányai ◽

Béla Illés

Keyword(s):

Black Hole ◽

Supply Chain ◽

Optimization Problems ◽

Optimization Method ◽

Chain Model ◽

Supply Chain Optimization ◽

Proposed Model ◽

Supply Chain Model ◽

Supply Model ◽

Black Hole Algorithm

The globalization of economy and market led to increased networking in the field of manufacturing and services. These manufacturing and service processes including supply chain became more and more complex. The supply chain includes in many cases consignment stores. The design and operation of these complex supply chain processes can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on metaheuristic algorithms. This research proposes an integrated supply model based on consignment stores. After a careful literature review, this paper introduces a mathematical model to formulate the problem of consignment-store-based supply chain optimization. The integrated model includes facility location and assignment problems to be solved. Next, an enhanced black hole algorithm dealing with multiobjective supply chain model is presented. The sensitivity analysis of the heuristic black hole optimization method is also described to check the efficiency of new operators to increase the convergence of the algorithm. Numerical results with different datasets demonstrate how the proposed model supports the efficiency, flexibility, and reliability of the consignment-store-based supply chain.

Download Full-text

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

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2018.7154 ◽

2018 ◽

Vol 15 (2) ◽

pp. 739-743 ◽

Cited By ~ 3

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.

Download Full-text

Adoption of Dual Iris and Periocular Recognition for Human Identification

Research Advances in the Integration of Big Data and Smart Computing - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-8737-0.ch014 ◽

2016 ◽

pp. 250-266

Author(s):

R. Deepika ◽

M. R. Prasad ◽

Srinivas Chetana ◽

T. C. Manjunath

Keyword(s):

Iris Recognition ◽

Recognition System ◽

Personal Identification ◽

Iris Segmentation ◽

Biometric System ◽

Multiple Features ◽

Facial Region ◽

Multimodal Biometric System ◽

Segmentation Approach ◽

Periocular Recognition

Personal identification from the iris images acquired under less-constrained imaging environment is highly challenging. Such environment requires the development of efficient iris segmentation approach and recognition strategy which can exploit multiple features available for the potential identification. So, along with the iris features periocular features have increasing attention in biometrics technology. For the recognition purpose iris and periocular information are collected from both the eyes of same person simultaneously. The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of image for periocular biometric is expected to require less subject cooperation. In this chapter, a dual iris based multimodal biometric system that increases the performance and accuracy of the typical iris recognition system is proposed.

Download Full-text

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i2.pp731-735 ◽

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

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.

Download Full-text

Iris segmentation using a new unsupervised neural approach

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i1.pp58-64 ◽

2020 ◽

Vol 9 (1) ◽

pp. 58

Author(s):

Hicham Ohmaid ◽

S. Eddarouich ◽

A. Bourouhou ◽

M. Timouyas

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Iris Recognition ◽

Recognition System ◽

Automatic Recognition ◽

Biometric Identification ◽

Iris Segmentation ◽

Biometric System ◽

Canny Edge ◽

Epigenetic Patterns

A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristic possessed by an individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris. Owing to the unique epigenetic patterns of the iris, Iris recognition is considered as one of the most accurate methods in the field of biometric identification. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction the iris. The segmentation algorithm proposed in this article starts with determining the regions of the eye using unsupervised neural approach, after the outline of the eye is found using the Canny edge, The Hough Transform is employed to determine the center and radius of the pupil and the iris.

Download Full-text

Black Hole Algorithm for Sustainable Design of Counterfort Retaining Walls

Sustainability ◽

10.3390/su12072767 ◽

2020 ◽

Vol 12 (7) ◽

pp. 2767 ◽

Cited By ~ 13

Author(s):

Víctor Yepes ◽

José V. Martí ◽

José García

Keyword(s):

Black Hole ◽

Sustainable Design ◽

Retaining Walls ◽

The Other ◽

Trade Off ◽

Construction Company ◽

Geometric Variables ◽

The Stability ◽

The Cost ◽

Black Hole Algorithm

The optimization of the cost and CO 2 emissions in earth-retaining walls is of relevance, since these structures are often used in civil engineering. The optimization of costs is essential for the competitiveness of the construction company, and the optimization of emissions is relevant in the environmental impact of construction. To address the optimization, black hole metaheuristics were used, along with a discretization mechanism based on min–max normalization. The stability of the algorithm was evaluated with respect to the solutions obtained; the steel and concrete values obtained in both optimizations were analyzed. Additionally, the geometric variables of the structure were compared. Finally, the results obtained were compared with another algorithm that solved the problem. The results show that there is a trade-off between the use of steel and concrete. The solutions that minimize CO 2 emissions prefer the use of concrete instead of those that optimize the cost. On the other hand, when comparing the geometric variables, it is seen that most remain similar in both optimizations except for the distance between buttresses. When comparing with another algorithm, the results show a good performance in optimization using the black hole algorithm.

Download Full-text