Open access

Introductory Chapter: On Biometrics with Iris

Written By

Muhammad Sarfraz and Nourah Alfialy

Submitted: 14 April 2022 Published: 27 July 2022

DOI: 10.5772/intechopen.105134

From the Edited Volume

Recent Advances in Biometrics

Edited by Muhammad Sarfraz

Chapter metrics overview

135 Chapter Downloads

View Full Metrics

1. Introduction

Biometrics is the systematic study of measuring and analyzing biological data for the purpose of validation or identification. Biometrics refers to specific physiological and/or behavioral (extrinsic and/or intrinsic respectively) characteristics that are uniquely related to a person [1, 2]. The biometric systems use unique human physiological and anatomical properties to define details. Such systems effectively help to overcome the security issues affecting the conventional methods of personal authentication. In the smart world today, the importance of technological solutions in biometrics is growing. Specifically, automation culture has desired to design and launch automated systems for highly reliable and accurate human authentication and identification.

Biometrics has been deployed successfully in various fields of real life. Numerous methods, techniques, and systems on biometrics serve sciences, security, military, medical area, and human identification. There are various kinds of biometrics being used, these include Fingerprint, Face, Speaker/Voice, Infrared thermogram (facial, hand, or hand vein), Gait, Keystroke, Odor, Ear, Hand geometry, Retina, Iris, Palmprint, Signature, DNA, Knuckle crease, Brain/EEG, Heart sound/ECG. Specifically, in the past decade, iris recognition technology has become the most popular biometric technology for human authentication and recognition due to its stability and uniqueness in its structure. The iris has a unique structure that remains stable throughout a person’s life. Iris recognition is one of the authentication methods that uses high-resolution assisted pattern recognition technology. The general method of the iris recognition system includes image acquisition, segmentation, feature extraction, matching, and classification [3].

Iris has become a very effective recording of its superior properties, such as reliability and accuracy. In recent years, a good amount of research is made regarding the evolution of biometric-based on the iris. This presented iris recognition as a very clear and effective concept. There is a need to highlight and analyze the work done by different authors related to iris studies, methodologies, and practices. A detailed comparative study could particularly provide an overview for the readers.

The idea of iris recognition goes back to an eighteenth-century Paris prison, where police distinguished criminals by examining the color of their irises. Daugman [4] was the first to develop the basic algorithms that now form the basis of all current commercial iris recognition systems, having been commissioned by Flom and Safir [5, 6] to conduct extensive and comprehensive research to implement automatic iris recognition. In 1987, Flom and Safir acquired a non-applied concept of an automated iris biometric system. A report was published by Johnston in 1992 without any experimental results [5, 6].

The motivation behind this work is to study biometric iris recognition specifically because it provides one of the most stable biometric signals to recognize distinct tissues that form prematurely and remain constant throughout life unless there is an eye injury.

The aim of this chapter is to contribute in a comprehensive survey about the difference between the existing biometrics techniques. An important aspect of biometric technology is to evaluate its performance. The performance of any biometric authentication technology can be measured by various parameters. Compared to other vital features, such as the face, fingerprint, and voice, the iris patterns are more stable and reliable [7, 8, 9]. The reason behind this is that iris recognition algorithms require pre-processing of the input image to obtain better data quality by tracking different feature points of the iris. Biometrics using a feature is so unique that the chance of any two people having the same features is very rare [10]. Identification of a person based on recognition of the iris of the eye gives one of the most reliable results. Iris tissue features provide unique high-dimensional information that explains why iris recognition-based verification has the lowest false acceptance rate among all types of identity verification systems [1, 11, 12, 13].

Iris recognition has been used in many countries with the purpose of identifying millions of people around the world. This technology is comfortable to use and difficult to rig. Many authentication programs, including border crossings without a passport, national identity, etc., have adopted this technology for its benefits [14]. For the purpose of human recognition, the iris biometric recognition system has proven its importance. The biometric iris recognition systems are easy to use and create a hassle-free security environment. Iris scanners can be used to protect high-value websites by blocking the access of unwanted visitors. Commercial and governmental institutions in all fields have recognized the benefits of this system and have embarked on implementing validation systems based on iris recognition in a major way [15]. Iris recognition is one of the best-protected methods of authentication and recognition. Iris recognition accuracy is very promising. The false acceptance rate, as well as the rejection rate, is very low. A special grayscale camera is used to take an iris pattern within 10–40 cm from the camera [14, 16, 17, 18]. An appropriate methodology is used to define the irises of the image, and if it is present, a grid of curves covering the iris is created and the iris symbol is generated based on the opacity of the points. It is affected by two things—first, the general opacity of the image, and secondly, the changes in the size of the iris. The comparison of two irises can be computed through the knock distance based on the difference in the number of bits and it is very fast [4, 19]. Also, the template matching technique can be used, and it uses statistical calculation to match the stored iris template and the obtained iris template. Iris recognition is applied in the following areas: border control, passports, ID cards, and other government purposes, database access, login authentication, aviation security, hospital security, access control to buildings, areas, homes, and security of restricted prisons [6, 20, 21].

For convenience, it is desired to know the basic concepts and terminologies we are going to use throughout this chapter. There are as follows:

  • Biometric: Originated from the Greek words bios (life) and metric or (measure), directly translates into “life measurement” [22].

  • Iris: It is a circular shape structure in the eye.

  • Iris normalization: “Performed to convert the iris coordinates into polar coordinates to rectangular iris template to make it constant and persistent against the effect of changing the size of the pupil. Once the outer and inner circles of the iris are localized, these values are taken as input to the Daugman’s Rubber-sheet model” [23].

  • Feature extraction: “After pre-processing of the image, feature extraction is carried out on normalized iris image” [23].

  • Daugman’s approach: Daugman’s patent states that “the system acquires through a video camera a digitized image of an eye of the human to be identified” [6].

  • The iris boundary is explained with parameters, which are the radius and the coordinates of the center of the circle of the iris boundary. Daugman proposed the integrodifferential operator to detect the iris boundary by finding the parameter space [6].

This chapter has been organized in various sections. Section 1 gives a brief introduction about biometric iris recognition, motivation to work in this study, the importance of this study, basic concepts and terminologies to be used, and the organization of this study. Section 2 consists of a literature survey and a comparative study of the existing different methods used in biometric iris recognition. It also gives information about different methods used in the extraction of the features of iris image datasets and data analysis. Finally, Section 3 gives the new directions for the future. It suggests some recommendations for community, government, industry, etc. Then the overall conclusion of the study is done in this chapter. It concludes with the discussion on future trends as well.

Advertisement

2. Literature survey

Although many papers have been published in this field in the past years, twenty-six papers have been selected and presented to understand the iris recognition techniques available in the literature. These articles have shown differences between each other in one way or another. In this chapter, a review is presented focusing on all four phases, i.e., segmentation, normalization, extraction, and template algorithms of the iris recognition technology from Daugman’s initial work in 1993 to some recent work.

Daugman [10] developed a feature extraction process based on information from a group of 2D Gabor filter. He created a file 256 bytes by specifying the local phase angle according to outputs of the real and imaginary parts of the filtered image, compare the percentage of mismatched bits between a pair of Iris representations via the XOR operator, and the choice of a separation point in the space of the Hamming distance.

On the contrary, Wildes system took advantage of the Laplacian pyramid, which was built with four different precision levels. Generate the iris symbol [14, 15]. Also, it explained a normalized correlation based on goodness-of-match values and Fisher’s linear discriminant for pattern matching. Both iris recognition systems use of bandpass image decompositions to get multi-scale information.

Lim et al. [21] proposed an iris recognition system. It includes a compact representation scheme for iris patterns by the 2D wavelet transform. This method is used for initializing weight vectors and determining winners for recognition in a competitive learning method. Flom and Safir [6] had earned a patent in Iris Recognition System, which gives a generalized concept in using iris as a biometric system but does not describe any implemented algorithm.

In the process of recognizing the iris of the eye, conversion is necessary. An iris image acquired in a convenient symbol can easily manipulate it. Hence, we will take a quick look at the process of feature extraction and representation of modern wonderful works and papers. Iris recognition is the procedure of comparing known and unknown irises to prove that it is from the same person or not [11].

Today, many approaches, techniques, and systems are used to match iris and solve related problems. This section is focused on analyzing and categorizing different author’s work in the iris recognition area. Table 1 provides a summary of various papers in the current literature. First column determines the Reference of the papers by author names and year of publication. Second column gives the summary of the work in the corresponding paper, and the third column describes the implemented approaches used to solve iris recognition issues. The author names and the year of publication have been used as an identifier for the rest of the tables in the chapter showing other details of the referred literature.

ReferenceBrief summary of the paperApproaches adopted
Choudhary, Tiwari and Singh, 2012 [3]Available feature extraction methods for iris pattern are studied in this paper. This paper is an analysis of the result of the various feature extraction methods. Iris localization using Hough transform performs better as compared to other localization techniques in case of occlusion due to eyelids and eyelashes.
  • Corner Detection Based Iris Encoding.

  • Feature extraction using Haar wavelet.

  • Feature extraction using Gabor filter.

  • Statistical pattern recognition

  • Multichannel Gabor Filter

Rakesh and Khogare, 2012 [5]In the feature extraction process, Gabor wavelet and wavelet transform, which are widely used for extracting features, were evaluated. From this evaluation, they found that Haar wavelet transform has better performance than that of Gabor transform. Second, Haar wavelet transform was used for optimizing the dimension of feature vectors in order to reduce processing time and space. They could present an iris pattern without any negative influence on the system performance. Last, they improved the accuracy of a classifier, a competitive learning neural network, by proposing an initialization method of the weight vectors and a new winner selection method designed for iris recognition. With these methods, the iris recognition performance increases to 98.4%.
  • Independent Component Analysis.

  • Multichannel Gabor filtering and 2D wavelet transforms.

  • Zero-crossing Representation Method

  • Iris Recognition Using Cumulative-Sum-Based Change Analysis.

  • Iris Recognition through Improvement of Feature vector and classifier

Arrawatia, Mitra and Kishore, 2017 [23]This paper offers review on existing technologies for iris recognition proposed by various researchers. Iris localization and segmentation, wavelets are used impressively, and Gabor filters are used for coding. There are two other popular techniques for segmentation: canny edge detector and Hough transform. But after adding Contourlet transform with the Hough transform and canny edge detector gives better results in segmentation which rates up to 100 percent. The comparison of result shows that this method for segmentation gives much better result for iris image segmentation with high accuracy and efficiency, which maintain the basic quality of image. For iris normalization, Daugman’s rubber-sheet model achieves better result by reducing dimensional inconsistencies.
  • Image Capturing/Acquisition

  • Iris Segmentation and Localization

  • Iris Image Denoising by Contourlet Transform

  • Normalization Stage

  • Feature Extraction

  • Feature Coding

  • Matching Algorithm

Bowyer, Hollingsworth and Flynn, 2008 [6]This survey suggests a structure for the iris biometrics literature and summarizes the current state-of-the art. Most research publications can be categorized as making their primary contribution to one of the four major modules in iris biometrics: image acquisition, iris segmentation, texture analysis and matching of texture representations. Other important research includes experimental evaluations, image databases, applications and systems, and medical conditions that may affect the iris.Flom and Safir’s concept patent
  • Daugman’s approach

  • Wildes’ approach

Sheela and Vijaya, 2010 [18]In this paper, different iris recognition methods, which aid an appropriate outlook for future work to build integrated classifier on the latest input devices for excellent business transactions, are discussed. Benchmark databases, products are also discussed. Since the area is currently one of the most on the go and the bulk of research is very large, this survey covers some of the significant methods.
  • Phase-based method

  • Texture analysis based method

  • Zero-crossing representation method

  • Approach based on intensity variations

Sanjay, Ganorkar, Ashok and Ghatol, 2007, 2004 [20]This paper presents a literature survey on the various techniques involved in identification and the emphasis given on biometric recognition system. In various applications, the biometric recognition system has been proved to be accurate and very effectively.
  • Sensor Module/Image Acquisition

  • Feature Extraction Module

  • Database Module

  • Matching Module

Daugman and Downing, 2001 [10]This paper investigated the randomness and uniqueness of human iris patterns by mathematically comparing 2.3 million different pairs of eye images. The phase structure of each iris pattern was extracted by demodulation with quadrature wavelets spanning several scales of analysis. The resulting distribution of phase sequence variation among different eyes was precisely binomial, revealing 244 independent degrees of freedom.
  • Complex-valued two-dimensional (2D)

  • Gabor Wavelets

  • The phase-quadrant demodulation process

Daugman, 2004 [11]Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b mm2 over the iris, enabling real-time decisions about personal identity with extremely high confidence.
  • Demodulation

  • Focus Assessment

  • Gabor wavelets

Sanjay, Ganorkar, Ashok and Ghatol, 2007 [20]In this paper, the system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.
  • Binary Segmentation

  • Pupil Center Localization

  • Circular Edge Detection

  • Remapping of the Iris

Anil, Ross and Prabhakar, 2004 [22]In this paper, a brief overview of the field of biometrics is given, and it summarizes some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
  • Sensor Module

  • Feature Extraction Module

  • Matcher Module

  • System Database Module

Bramhananda, Reddy and Goutham, 2018 [12]This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multimodel biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.
  • Hamming Distance Classifier (HDC) for predicting False Rejection Rate (FRR) and False Acceptance Rate (FAR)

Roy and Bandyopadhyay, 2017 [1]This paper highlighted the detection of iris using biotechnology technique.
Phadke, 2013 [13]This paper discussed various Biometric Identification Systems which can be grouped based on the main physical characteristic that lends itself to biometric identification; Fingerprint identification, Hand geometry, Palm Vein Authentication, Retina scan, Iris scan, Face recognition, Signature, Voice analysis.
Rui and Yan, 2018 [24]In this paper, the authors classified and thoroughly review the existing biometric authentication systems by focusing on the security and privacy solutions. They had analyzed the threats of biometric authentication and proposed several criteria with regard to secure and privacy-preserving authentication. They had further reviewed the existing works of biometric authentication by analyzing their differences and summarizing the advantages and disadvantages of each based on the proposed criteria. This paper discussed the problems of aliveness detection and privacy protection in biometric authentication.
Jin-Hyuk, Eun-Kyung and Sung-Bae, 2004 [7]This paper gives a comprehensive overview of biometric technology and performance evaluation with more than 100 publications. After the thorough review, it proposed a promising evaluation method based on affecting factors.
Manisha and Kumar, 2019 [8]This research paper presented a comprehensive survey of more than 120 techniques suggested by various researchers for Cancelable Biometrics and a novel taxonomy for the same is developed. Further, various performance measures used in Cancelable Biometrics are reviewed and their mathematical formulations are given. It also suffers from various security attacks as given in literature. A review of these security attacks is carried out. It also performed a review of databases used in literature for nine different Cancelable Biometrics.
  • Cryptography based methods

  • Transformation based methods

  • Filter based methods

  • Hybrid methods

  • Multimodal based methods

Himanshu, 2013 [9]This paper proposed a personal identification using iris recognition system with the help of six major steps, which are image acquisition, localization, isolation, normalization, feature extraction, and matching, and these six steps consists several minor steps to complete each step. The boundaries of the iris, as papillary and limbic boundary, are detected by using Canny Edge Detector & Circular Hough Transformation. It used masking technique to isolate the iris image from the given eye image, this isolated iris image is transformed from Cartesian to polar coordinate. Finally extract the unique features of the iris after enhancing the iris image and then perform matching process on iris code using Hamming Distance for acceptance and reject process.
  • Canny Edge Detector & Circular Hough Transformation

  • Localization

  • Enhancement and Denoising

  • Feature Extraction

Williams, 1997 [25]This paper discussed Iridian Technologies systems that have enrolled 99.99% of the irises presented to them. This Technologies iris recognition system has allowed no False Accept errors in over three million file comparisons during the two testing programs referenced in this paper, and in millions of file comparisons elsewhere. Under the formal, controlled DOD testing scenario, the system was 99.95% accurate in the area of False Rejects, with only one False Reject out of 1,995 trials. The reason for that error was identified, corrected, and never repeated.
  • Hamming Distance Calculation

  • Recognition or Rejection

Wildes, 1997 [15]This paper examines automated iris recognition as a biometrically based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides interesting structure on which to base a technology for noninvasive biometric assessment.
  • Image Acquisition

  • Iris Localization

  • Pattern Matching

Wildes, Asmuth, Green, Hsu, Kolczynski, Matey and McBride, 1994 [14]This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement.
  • Image Acquisition

  • Iris Localization

  • Pattern Matching

Ganorkar and Ghatol, 2007 [19]In this paper, iris recognition as one of the important method of biometrics-based identification systems and iris recognition algorithm is described. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality.
  • Binary Segmentation

  • Pupil Center Localization

  • Circular Edge Detection

  • Remapping of the Iris

Daugman, 1993 [4]This paper studies the method for rapid visual recognition of personal identity based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris. The visible texture of a person’s iris in a real-time video image is encoded into a compact sequence of multi-scale quadrature 2-D Gabor wavelet coefficients, whose most-significant bits comprise a 256-byte. Statistical decision theory generates identification decisions from Exclusive-OR comparisons of complete iris codes at the rate of 4000 per second, including calculation of decision confidence levels.
  • Complex-valued two-dimensional (2D)

  • Gabor wavelets

  • The phase-quadrant demodulation process

Lim, Lee, Byeon and Kim, 2001 [21]This paper proposed an efficient method for personal identification by analyzing iris patterns that have a high level of stability and distinctiveness. In order to improve the efficiency and accuracy of the proposed system, it presented a new approach to making a feature vector compact and efficient by using wavelet transform, and two straightforward but efficient mechanisms for a competitive learning method such as a weight vector initialization and the winner selection. With all these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.
  • Image Acquisition

  • Pre-processing Stage

  • Feature Extraction Stage

  • Identification and Verification Stage

  • Wavelet Transform

  • Gabor Transform

Table 1.

An overview of literature.

The main point of biometrics technology is to evaluate their performance and accuracy. It can be measured by the various parameters such as False Accept Rate (FAR), False Reject Rate (FRR) and Crossover Rate (CER) or Equal Error Rate (EER). An identity claims wrongly rejected is called False Rejection and a false identity claims wrongly accepted is known as False Acceptance. In order to make limited entry to authorized users FAR and FRR are used. False Rejection Rate (FRR) measures the probability of rejecting an authorized user incorrectly as an invalid user [16].

Table 2 shows the accuracy and performance in percentage. It also mentions the identification and verification measures. Identification and verification are matching techniques for Iris recognition. In the verification, the person enrolls his Iris to the system and the template is stored in the database. Every time the person accesses the system, he has entered his iris to verify himself. It’s a one-to-one relationship where the input Iris is compared with the stored one. On the other hand, identification is one to many relationships because the human Iris is matched with the Irises in 7the database to determine who is that person [11]. While the performance measures used for identification depend on the accuracy, recognition rate, rank K, etc., the performance measures for verification are False Match Rate (FMR), False Non-Match Rate (FNMR), False Accept Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (EER). The researchers in [3] describe the meaning of the authentication parameters. FAR happens when the system recognizes person erroneous. But when the system rejects entry to approve person that means the FRR is happening. FMR is the amount of fraud assessments with threshold value “T” divided by the total quantity of fraud similarities. FNMR is the quantity with unaffected comparisons with threshold value “T” divided by the total quantity of open comparisons. Last one is EER; it describes the error rate of the system.

ReferenceAccuracy (Performance)Performance measures used for verificationPerformance measures used for identification
Choudhary, Tiwari and Singh, 2012 [3]
  • 95% (Singh et al.)

  • 95.4% (Gupta et al.) FAR/FRR: 4/5

  • 96.3% (Greco et al.) FAR/FRR: 3/4

  • 94.85% (Tuama) 2.43/3.17

  • 95.68% (Li Ma)

FAR, FRRAccuracy, Recognition Rate, Iris Normalization, Pattern Matching, Feature Extraction, and Training & Testing Time.
Rakesh and Khogare, 2012 [5]
  • 98.4%

EERBiometric, Gabor filtering, wavelet transform, cumulative sum, zero-crossing and feature vector.
Arrawatia, Mitra and Kishore, 2017 [23]
  • 100%

FAR, FRRImage Acquisition, Localization, Segmentation, Normalization, Feature Extraction, Template Generation, Pattern Matching.
Bowyer, Hollingsworth and Flynn, 2008 [6]
  • Daugman’s algorithm performed the best with 99.90% accuracy

  • Ma’s algorithm with 98.00%

  • Avila’s algorithm with 97.89%

  • Tisse’s algorithm with 89.37%

FAR,FRR, EERTrue Accept, False Accept, Receiver Operating Characteristic, Cumulative Match Characteristic
Sheela and Vijaya, 2010 [18]
  • The experiments were conducted on UBIRIS database with accuracy of 98.02 and 97.88% for images captured in session 1 and session 2, respectively.

  • The segmentation performance for 1214 good quality images and 663 noisy images was 98.02 and 97.88%, respectively.

FAR, FRRPhase-based method, Texture-analysis, Zero-crossing, Local intensity variations, Independent Component Analysis, Continuous Dynamic Programming.
Sanjay, Ganorkar, Ashok and Ghatol, 2007, 2004 [20]Iris Recognition Performance Evaluation:
  • 0.94% (FAR)

  • 0.99% (FRR)

  • 0.01% (CER)

  • 0.50% (FTE)

FAR, FRR, CER, EERFalse Rejection, False Acceptance, Face Recognition Vendor Test (FRVT).
Daugman and Downing, 2001 [10]
  • 95%

Daugman, 2004 [11]
Sanjay, Ganorkar, Ashok and Ghatol, 2007 [20]
  • 93%

Anil, Ross and Prabhakar, 2004 [22]
  • 99%

FER, FTE, FTC, FNMR, FMRFalse Non-Match Rate, False Match Rate
Bramhananda, Reddy and Goutham, 2018 [12]
Roy and Bandyopadhyay, 2017 [1]
Phadke, 2013 [13]
Rui and Yan, 2018 [24]FAR, FRR, EER
Jin-Hyuk, Eun-Kyung and Sung-Bae, 2004 [7]FAR, FRR, FMRFTE, FTA, FMR/FNMR, FAR/FRR, and FR for each trial are used to estimate the recognition performance, and processing time, efficiency of matching algorithm, performance for a specific population are used to analyze the results.
Manisha and Kumar, 2019 [8]FAR, FPRFailure to Acquire Rate (FTAR), Failure to Capture Rate (FTCR), FMR and FTA
Himanshu, 2013 [9]
  • 98.9%

Williams, 1997 [25]
  • 99.95%

Crossover (Equal) Error Rate (CER), Recognition Speed, Enrollment, Confidence, Testing
Wildes, 1997 [15]
Wildes, Asmuth, Green, Hsu, Kolczynski, Matey and McBride, 1994 [14]
Ganorkar and Ghatol, 2007 [19]
  • 93%

Daugman, 1993 [4]
Lim, Lee, Byeon and Kim, 2001 [21]
  • 97.1% (Learning Data)

  • 95.9% (Test Data)

  • Overall Performance 97.1–98.4%.

FAR, FPRLearning Data, Test Data, Wavelet transform, Gabor transform, Multi-dimensional Winner Selection, Euclidean distance-based winner selection

Table 2.

An overview of literature for Accuracy and performance.

In Lim et al. [21], eye images captured at a distance with the help of a CCD camera. Then, in the acquired image, iris is segmented. Initially, it is done by detecting the pupil using the center point detection method followed by edge detection method by finding virtual circles. An analysis was made in the pre-processing stage, with 6000 data to identify the causes of failure at this stage. Data involved images both with Lens, without, and with glasses. In the normalization stage a 450×60 bit iris image part was obtained. Gabor transforms and Haar wavelet transforms, which are two different methods, were used to analyze and extract the features from the segmented iris image.

In Daugman [11], proposed an approach that is an improvement to his previous work. This approach is working with the noise disturbances that occur while acquiring an iris image of a human eye. Also, an algorithm was introduced for detecting the eyelids, which involves arcuate edges with spline parameter, instead of circular edges in the Integro differential operator.

In Wilde [14, 15] tried a different approach, in which inner and outer iris boundary is computed with the help of a gradient-based binary edge map followed by circular Hough transform. Wilde used around 60 human irises captured from 40 subjects in his experiment. Also, he has done a comparative study with Daugman’s work in his paper. This method proved to provide higher accuracy rate when tested in CASIA database.

These algorithms can provide rotation, translation and size invariant result. Simulation results of these algorithm prove to provide a higher correct accept and reject rate. Results were tested using CASIA database, UBIRIS, UPOL, MMU and a database provided by Institute of Automation for 2005 Biometrics Authentication competition.

The experimental parts of the author’s [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] are shown in Table 3. It explains the type of applications and kind of Databases used. Then, it shows the number of data used in the study.

ReferenceBiometricDatabaseNo. of identities
Choudhary, Tiwari and Singh, 2012 [3]
  • Iris

  • CASIA

  • 1024

Rakesh and Khogare, 2012 [5]
  • Iris

  • UBIRIS

  • CASIA-IrisV3

  • ND 2004–2005 database

  • 241

  • 1500

Arrawatia, Mitra and Kishore, 2017 [23]
  • Iris

  • CASIA

  • LEI

  • UPOL

Bowyer, Hollingsworth and Flynn, 2008 [6]
  • Iris

  • CASIA 1

  • CASIA 3

  • ICE2005

  • ICE2006

  • UBIRIS

  • UPOL

  • 108

  • 1500

  • 244

  • 480

  • 241

  • 128

Sheela and Vijaya, 2010 [18]
  • Iris

  • UBIRIS V1

  • UBIRIS V2

  • CASIA V1

  • CASIA V2

  • CASIA V3-Interval

  • CASIA V3-Lamp

  • CASIA V3-Twins

  • ND 2004–2005

  • Iris DB 400

  • Iris DB 800

  • Iris DB 1600

  • UPOL

  • MMU1

  • MMU2

  • 241

  • 261

  • 108

  • 60

  • 249

  • 411

  • 200

  • 356

  • 200

  • 400

  • 800

  • 64

  • 100

  • 100

Sanjay, Ganorkar, Ashok and Ghatol, 2007, 2004 [20]
  • Fingerprint

  • Face

  • Retina

  • Iris

  • Hand Geometry

  • DNA

  • Ear

  • Body Odor

  • Palm Print

  • Lip Motion

  • Hand Vein

  • Gait

  • Signature

  • Voice

Daugman and Downing, 2001 [10]
  • Iris

Daugman, 2004 [11]
  • Iris

Sanjay, Ganorkar, Ashok and Ghatol, 2007 [20]
  • Iris

  • CASIA

Anil, Ross and Prabhakar, 2004 [22]
  • Iris

  • DNA

  • Face

  • Hand and finger geometry

  • Fingerprint

  • Signature

  • Voice

  • Retinal scan

Bramhananda, Reddy and Goutham, 2018 [12]
  • Iris

  • Face

  • Fingerprint

Roy and Bandyopadhyay, 2017 [1]
  • Iris

Phadke, 2013 [13]
  • DNA

  • Fingerprint

  • Hand Geometry

  • Hand Vein

  • Iris Face/Facial Thermo Gram

  • Physiological

  • Retinal Scan

  • Signature

  • Voice

Rui and Yan, 2018 [24]
  • Iris

  • Fingerprint

  • Voice

  • Keystroke

  • Face

Jin-Hyuk, Eun-Kyung and Sung-Bae, 2004 [7]
  • Iris

  • Fingerprint

Manisha and Kumar, 2019 [8]
  • Fingerprint

Himanshu, 2013 [9]
  • Iris

  • CASIA

  • MMU

Williams, 1997 [25]
  • Iris

Wildes, 1997 [15]
  • Iris

Wildes, Asmuth, Green, Hsu, Kolczynski, Matey and McBride, 1994 [14]
  • Iris

Ganorkar and Ghatol, 2007 [19]
  • Iris

  • CASIA

Daugman, 1993 [4]
Lim, Lee, Byeon and Kim, 2001 [21]

Table 3.

An overview of literature for data used.

There is a need to overlook for the data images together with their resolution and format. Table 4 describes the number of data used in the application, the number of images resulted, their resolutions and formats.

ReferenceTotal no. of imagesResolution (in pixels)Image format
Choudhary, Tiwari and Singh, 2012 [3]
  • 60

  • 80×360

Rakesh and Khogare, 2012 [5]
  • 1877

  • 22,051

  • 6000

  • 64×300

Arrawatia, Mitra and Kishore, 2017 [23]
  • 10 pixels and angular resolutions with angles varying from 00 to 3600

Bowyer, Hollingsworth and Flynn, 2008 [6]
  • 756

  • 22,051

  • 2953

  • 60,000

  • 1877

  • 384

Sheela and Vijaya, 2010 [18]
  • 1877

  • 11,102

  • 756

  • 1200

  • 2655

  • 16213

  • 3183

  • 64,980

  • 8,000

  • 16,000

  • 32,000

  • 384

  • 450

  • 995

  • 400×300

  • 800×600

  • 320×280

  • 640×480

  • 320×280

  • 640×480

  • 640×480

  • 640×480

  • 1280×960

  • 1280×960

  • 1280×960

  • 576×768

  • 320×280

  • 320×280

  • jpeg

  • jpeg

  • bmp

  • bmp

  • jpeg

  • jpeg

  • jpeg

  • tiff

  • bmp

  • bmp

  • bmp

  • png

  • bmp

  • bmp

Sanjay, Ganorkar, Ashok and Ghatol, 2007, 2004 [20]
Daugman and Downing, 2001 [10]
  • 2150

  • 640×480

Daugman, 2004 [11]
  • 640×480

Sanjay, Ganorkar, Ashok and Ghatol, 2007 [20]
  • 51

Anil, Ross and Prabhakar, 2004 [22]
Bramhananda, Reddy and Goutham, 2018 [12]
Roy and Bandyopadhyay, 2017 [1]
Phadke, 2013 [13]
Rui and Yan, 2018 [24]
Jin-Hyuk, Eun-Kyung and Sung-Bae, 2004 [7]
Manisha and Kumar, 2019 [8]
Himanshu, 2013 [9]
Williams, 1997 [25]
Wildes, 1997 [15]
Wildes, Asmuth, Green, Hsu, Kolczynski, Matey and McBride, 1994 [14]
Ganorkar and Ghatol, 2007 [19]
  • 51

Daugman, 1993 [4]
Lim, Lee, Byeon and Kim, 2001 [21]
  • 6000

  • 450×60

Table 4.

An overview of literature for data images with resolution and format.

In [10], Daugman described how iris recognition is being used to check visitors coming to the United Arab Emirates (UAE) against a watch-list of people who are denied entry to this country. The UAE database contains around 632,500 different iris images. In all comparison, no false matches were found with Hamming distances below about 0.26. Daugman reports that “to date, some 47,000 persons have been caught trying to enter the UAE under false travel documents, by this iris recognition system” [4, 17]. There are similar reports for various kinds of applications and methodologies. Table 5 describes the implemented application type and the reason for using it by mentioning the advantages and disadvantages of the proposed methods.

ReferenceApplicationReason of applicationAdvantagesDisadvantages
Choudhary, Tiwari and Singh, 2012 [3]
Rakesh and Khogare, 2012 [5]
  • Computer System Security

  • Secure Electronic Banking

  • Mobile phones

  • Credit cards

  • Iris patterns have stable, invariant, and distinctive features for personal identification.

Arrawatia, Mitra and Kishore, 2017 [23]
  • Finance and banking

  • Healthcare and welfare

  • Immigration and border control

  • Public safety

  • Point of sale and ATM

  • Hospitality and tourism

  • For verification and identification

  • Identify the accurate patient.

  • For security purpose the iris recognition technique is used in many countries borders and airports.

  • Some law enforcement agencies save the criminal data to track them.

  • The vulnerable Pos terminal is hacked by a hacker for the regular payment. For this activity, they are using the skimmers. These skimmers are installed at terminals which read and transmit the information of swiped card.

  • To overcome the unwanted access of a user in hotel room.

  • It is less time consuming and improves the standard of service and the customer or user will free from document verification process for identification, which is more time consuming.

  • Provides a high accuracy then other biometric technique and removal of delicacy in medical record of a person.

  • Utilizing the security and accuracy of iris recognition system these agencies track the terrorist & criminals.

  • Law enforcement agencies use the saved biometric data of criminal record to enhance the security of public.

  • Iris recognition system used on all swipe or ATM machines so that hacker never use the information of others.

Bowyer, Hollingsworth and Flynn, 2008 [6]
  • “EyeCert” system

  • Issue identity cards to authorized users. The barcode on the cards would store both biometric information about the person’s iris, as well as other information, such as a name, expiration date, birth date, and so forth.

  • The system is designed to allow identity verification to be done offline, thus avoiding potential problems that would come with systems that require constant access to a centralized database.

Sheela and Vijaya, 2010 [18]
  • Civilian Identification management program

  • The Offender Identification System [Offender-ID]

  • PIER 2.4

  • The Handheld Interagency Identity Detection Equipment [HIIDE]

  • The LG Iris Access, Panasonic BM-ET200, Oki, IBM, Iris Guard IG-AD100, Sage, Spectrometric and Argus systems

  • Iris authentication product

  • Supports identification of prisoners in jail environment.

  • Provides mobile identification with iris technology in a real-time environment.

  • Multi-biometric handheld device. It is used in defense agencies and in remote or centralized enrolments

  • Work by analyzing the iris patterns and converting them into digital templates.

Sanjay, Ganorkar, Ashok and Ghatol, 2007, 2004 [20]
  • Border Control

  • Passports and Identity Cards.

  • Database Access

  • Login Authentication

  • Aviation Security

  • Hospital Security

  • Controlling access to restricted buildings, areas, homes and prison security

  • Iris recognition is one of the best-protected approaches for authentication and recognition,

  • The accuracy of Iris recognition is most promising.

  • The false acceptance rate as well as rejection rate is very low.

Daugman and Downing, 2001 [10]
Daugman, 2004 [11]
Sanjay, Ganorkar, Ashok and Ghatol, 2007 [20]
Anil, Ross and Prabhakar, 2004 [22]
  • Immigration and naturalization service accelerated service system (INSPASS)

  • Border passage system using iris recognition at London’s Heathrow airport.

  • The Face Pass system from Viisage is used in POS verification applications like ATMs

  • Ensure that the rendered services are accessed only by a legitimate user and no one else.

Bramhananda, Reddy and Goutham, 2018 [12]
Roy and Bandyopadhyay, 2017 [1]
Phadke, 2013 [13]
  • ID management solution controlled and operated by governments

  • Biometric identification can provide extremely accurate, secured access to information; fingerprints, retinal and iris scans produce absolutely unique datasets when done properly.

  • Iris scanning is less intrusive than retinal recognition because the iris is easily visible from several feet away.

Rui and Yan, 2018 [24]
Jin-Hyuk, Eun-Kyung and Sung-Bae, 2004 [7]
Manisha and Kumar, 2019 [8]
Himanshu, 2013 [9]
  • National border controls as living passport.

  • Computer login

  • Secure access to bank account at ATM machine

  • Ticketless travel

  • Authentication in networking

  • Permission access control to home, office, laboratory, etc.

  • Driving licenses, and other personal certificates

Williams, 1997 [25]
Wildes, 1997 [15]
Wildes, Asmuth, Green, Hsu, Kolczynski, Matey and McBride, 1994 [14]
Ganorkar and Ghatol, 2007 [19]
Daugman, 1993 [4]
Lim, Lee, Byeon and Kim, 2001 [21]

Table 5.

An overview of literature for their applications and advantageous features.

Advertisement

3. Conclusion

Biometrics means the automatic identification of a person based on his behavioral and/or physiological unique characteristics. Iris biometrics is an efficient, safe, cost-effective, easy-to-use technique for identity verification. This study provides detailed information related to iris recognition techniques. Several author’s works, related to iris recognition technology, are discussed, compared and analyzed. A detailed analysis of various studies is made. Various methods are taken into account to extract features of the iris such as wavelet beam analysis and static measurement feature transformation. The main focus is on iris as biometrics feature for the secure authentication and uniqueness of human identification around the world. The iris is one of the biophysiological features that are very reliable in identification systems. It is used in multimodal biometrics and in conjunction with cryptography. It is also considered one of the fairest biometrics of the face. However, it has been found that the localization of the iris is affected by tissue. When not properly interpreted, commercial iris-based biometrics systems provide inaccurate results while identifying humans. Moreover, it is important that the iris-based identification systems work with both ideal and imperfect iris images, otherwise safety will be at stake.

As a future work, there is a scope to improve the problems related to iris recognition, specially, the issues related to the capturing Iris by the sensors. One of the innovations is the touchless Iris sensors, which will be sufficient for various difficult situations including COVID-19 in the current time and age. It will decree the need to touch the devices. This technique is needed to show its reliability and efficacy as an alternative to regular sensors. Relying on an iris recognition in a different government domain is also recommended. Implementing iris recognition technology is not only useful for Government, but other organizations and communities can also think and may benefit by applying iris recognition techniques to identify and verify.

It has also been noted that iris-based biometric systems tend to present erroneous results in uncooperative settings. Another important idea is that the iris can be used for mobile phone communications with smart devices. Revocable biometrics is useful for strong security in the event of attacks. There are direct and indirect attacks on multimodal biometrics that must be overcome. More research is needed to know that attacks like these cannot break the security of biometric systems. With these ideas in mind, in the future, people can focus on designing ATMs with iris recognition in the banking industry.

There is also a need of the time to concentrate on using real apps to support the generation of tiny iris codes for cell phones and PDAs. In this chapter, an attempt is made to provide an insight into different iris recognition methods. Technology survey provides a platform for developing new technologies in this field as a future work.

References

  1. 1. Roy S, Bandyopadhyay SK. Iris recognition using biometric techniques. International Journal of Applied Research 2017;3(5):822-823
  2. 2. El-Abed M, Giot R, Hemery B, Rosenberger C. Evaluation of biometric systems: A study of users’ acceptance and satisfaction. International Journal of Biometrics, Inderscience. 2012. pp.1-27. 10.1504/IJBM.2012.047644. hal-00984024
  3. 3. Choudhary D, Tiwari S, Singh AK. A survey: Feature extraction methods for iris recognition. International Journal of Electronics Communication and Computer Technology. 2012;2(6):275-279
  4. 4. Daugman JG. High confidence recognition of persons by a test of statistical independence. IEEE Transaction on PAMI. 1993;15(11):1148-1161
  5. 5. Rakesh T, Khogare MG. Survey of biometric recognition system for Iris. International Journal of Emerging Technology and Advanced Engineering. 2012;2(6):272-276
  6. 6. Bowyer KW, Hollingsworth K, Flynn PJ. Image understanding for iris biometrics, a survey. Computer Vision and Image Understanding. 2008;110(2):281-307
  7. 7. Jin-Hyuk H, Eun-Kyung Y, Sung-Bae C. A review of performance evaluation for biometrics systems. International Journal of Image and Graphics. 2005;5(3):501-536
  8. 8. Manisha KN. Cancelable Biometrics: A comprehensive survey 2020;53:3403–3446. DOI: 10.1007/s10462-019-09767-8
  9. 9. Himanshu S. Personal identification using iris recognition system, A Review. International Journal of Engineering and Applications (IJERA). 2013;3:449-453
  10. 10. Daugman J, Dowing C. Epigenetic randomness, complexity, and singularity of human iris patterns. Biological Sciences. 2001:1737-1740
  11. 11. Daugman J. How Iris recognition works. IEEE Transaction on Circuits and Systems for Video Technology. 2004;14(1):21-30
  12. 12. Bramhananda Reddy MV, Goutham V. Iris Technology: A review on iris based biometric systems for unique human identification. International Journal of Research—Granthaalayah. 2018;6(1):80-90. DOI: 10.5281/zenodo.1162210
  13. 13. Phadke S. The importance of a biometric authentication system. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA). 2013;1(4):128-132
  14. 14. Wildes R, Asmuth J, Green G, Hsu S, Kolczynski R, Matey J, et al. A system for automated iris recognition. Sarasota, FL: Proceedings IEEE Workshop on Applications of Computer Vision; 1994. pp. 121-128
  15. 15. Wildes R. Iris recognition: An emerging biometric technology. Proceedings of the IEEE. 1997;85(9):1348-1363
  16. 16. Sabhanayagam T, Prasanna VV, Senthamaraikannan K. A comprehensive survey on various biometric systems. International Journal of Applied Engineering Research. 2018;13(5):2276-2297
  17. 17. Daugman. Recognizing persons by their Iris patterns. In: Biometrics: Personal Identification in Networked Society. Kluwer; 1998. pp. 103-121
  18. 18. Sheela SV, Vijaya PA. Iris recognition methods survey. IJCA. 2010;3(5):19-25
  19. 19. Ganorkar SR, Ghatol AA. Iris recognition: An emerging biometric technology. In: Proceedings of international Conference ICSCI 2007. Hyderabad; 2007. pp. 596-600
  20. 20. Sanjay R, Ganorkar AA, Ghatol. Iris recognition, an emerging biometric technology. In: Proceedings of 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Greece. 2007. pp. 91-96
  21. 21. Lim S, Lee K, Byeon O, Kim T. Efficient iris recognition through improvement of feature vector and classifier. ETRI Journal. 2001;23(2):61-70
  22. 22. Anil JK, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology. 2004;14(1):4-20
  23. 23. Arrawatia S, Kishore B, Mitra P. Critical literature survey on iris biometric recognition. International Journal of Scientific Research in Science and Technology IJSRST. 2017;3(6):600-605
  24. 24. Rui Z, Yan Z. A survey on biometric authentication: Toward secure and privacy-preserving identification. IEEE Access. December 2018;99:1-1. DOI: 10.1109/ACCESS.2018.2889996
  25. 25. Williams G. IRIS recognition technology. IEEE AES System Magazine. 1997:23-29

Written By

Muhammad Sarfraz and Nourah Alfialy

Submitted: 14 April 2022 Published: 27 July 2022