ArticlePDF Available

IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION

Authors:

Abstract and Figures

Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms. Since iris is unique for all individuals across the globe, many researchers focused on using iris or along with other biometrics for security with great precision. Multimodal biometric systems came into existence for better accuracy in human authentication. However, iris is considered to be most discriminatory of facial biometrics. Study of iris based human identification in ideal and non-cooperative environments can provide great insights which can help researchers and organizations that depend on iris-based biometric systems. The technical knowhow of iris strengths and weaknesses can be great advantage. This is more important in the wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light into various iris-based biometric systems, issues with iris in the context of texture comparison, cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging scenarios pertaining to accurate iris recognition and so on.
Content may be subject to copyright.
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [80]
Science
IRIS TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC
SYSTEMS FOR UNIQUE HUMAN IDENTIFICATION
Dr M V Bramhananda Reddy 1, Dr V Goutham 2
1, 2 Professor, Computer Science & Engineering, Sreyas Institute of Engineering & Technology,
JNTUH, Hyderabad, India
Abstract
Biometric features are widely used in real time applications for unique human identification. Iris
is one of the physiological biometric features which are regarded as highly reliable in biometric
identification systems. Often iris is combined with other biometric features for robust biometric
systems. It is also observed that biometrics is combined with cryptography for stronger security
mechanisms. Since iris is unique for all individuals across the globe, many researchers focused
on using iris or along with other biometrics for security with great precision. Multimodal
biometric systems came into existence for better accuracy in human authentication. However, iris
is considered to be most discriminatory of facial biometrics. Study of iris based human
identification in ideal and non-cooperative environments can provide great insights which can
help researchers and organizations that depend on iris-based biometric systems. The technical
knowhow of iris strengths and weaknesses can be great advantage. This is more important in the
wake of widespread use of smart devices which are vulnerable to attacks. This paper throws light
into various iris-based biometric systems, issues with iris in the context of texture comparison,
cancellable biometrics, iris in multi-model biometric systems, iris localization issues, challenging
scenarios pertaining to accurate iris recognition and so on.
Keywords: Security; Biometrics; Iris Recognition; Iris-Based Biometric Systems.
Cite This Article: Dr M V Bramhananda Reddy, and Dr V Goutham. (2018). IRIS
TECHNOLOGY: A REVIEW ON IRIS BASED BIOMETRIC SYSTEMS FOR UNIQUE
HUMAN IDENTIFICATION.” International Journal of Research - Granthaalayah, 6(1), 80-90.
https://doi.org/10.5281/zenodo.1162210.
1. Introduction
Biometrics is an automated approach which exploits measurable physiological, physical and
behavioural traits of humans for identification and authentication. Physiological and behavioral
are the two categories in biometrics. The former refers to hand and palm geometry, DNA, face,
iris, scent signature, keystroke dynamics and fingerprints while the latter refers to voice, gait, and
typing rhythm. The real world applications of biometrics include detection and border security,
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [81]
fraud prevention, crime tracking, security, payment systems, attendance recording, physical and
logical access controls, and identification of parties or individuals in general [28], [29].
Biometrics is one of the best ways in which individuals can be identified uniquely across the
globe. Biometrics can be used in cryptography to secure communications in the real world
networks [1].
Biometric templates when compromised, the security will be lost. To overcome this problem,
cancellable biometrics approach came into existence [3], [20], and [30]. Hamming Distance
Classifier (HDC) for predicting false rejection rate (FRR) and false acceptance rate (FAR) based
on the hamming distance threshold was proposed [4]. Ocular biometrics was given importance
by Simona and Arun [5].Studies were made on iris and face as biometric features to protect
communications in mobile devices [6]
It was noted from the literature that studies were made on binary iris code for reconstruction of
original iris image [7]. Investigations on iris and fingerprints together for human identification
were also carried out [8]. It is focused on the UID project in India named “Aaadhar”. The
investigations dealt the issues with biometric systems in the wake of security attacks on multi-
model biometric systems [9, 10, and 19]. Iris localization is very important activity in
commercial iris recognition systems. However, they could not perform well with ideal data as
they work for controlled data. Many iris localization experiments were performed [11-15]. Lee et
al. [16] made sensitivity analysis on biometric systems in the wake of attacks on such systems
that help in finding the robustness of biometric system. Combination of combined error
correction codes and finger prints [17]; multi-model biometric system using face and iris
combination [18], SVM and feature selection techniques [21], Circular Hough Transform and K-
Means algorithm [21], combination of different approaches [22] were used for iris recognition.
Reverse bio-orthogonal wavelet transform technique was used for reliable iris recognition [24].
Iris hazards in the presence of noise were explored [25]. Pattern recognition and its importance
in iris-based biometric system were presented by Unar et al. [26] while Zhu et al. [27] used iris
based biometric system for random number generator. Iris based biometric system has become
one of the most active search field sand it is driven by many applications towards authentication
sand recognitions of an individual identity. From the above literature, it can be noted that limited
studies were carried out on Iris Based Biometric Systems for Unique Human Identification.
In this paper, the concept and some of the important biometric systems which are Iris based are
deliberated along with the security issues of the iris based human identification systems.
The remainder of the paper is structure as follows. Section II reviews iris templates and
cancellable biometrics. Section III focuses on biometric binary strings. Section IV throws light
on multi-model biometrics. Section V presents GA for iris reconstruction. Section VI focuses on
security issues with biometric systems. Section VII and VIII discuss about iris localization and
iris recognition systems. Section IX concludes the paper.
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [82]
2. Iris Templates and Cancellable Biometrics
There is a study on iris-templates for crypto-biometric schemes as in [1]. This scheme helps
users to get secret keys by using her biometric template. Fuzzy extractors are used to make the
scheme robust. The scheme has both enrolment phase and verification phase. The enrolment
ensures polynomial security and verification phase, also has polynomial complexity, and takes
care of verifying the identity of users. Few authors focused on functional dual tree complex
wavelet for biometric security and its applications include transient signal processing, image
transmission, image compression and biometrics.
Biometric templates when compromised, the security will be lost. To overcome this problem,
cancellable biometrics approach came into existence. This will take care of transformation
functions in order to hide the original template. In this case the transformed biometric template
when compromised, the original template can be used to make new transformation [30]. Towards
cancellable biometrics as in [3] studied different fusion approaches in order to achieve
cancellable recognition with multi-biometrics. They focused three cancellable transformations on
two biometric modalities based on iris and voice. There is a methodology used for cancellable
biometrics approach which is as shown in Figure 1. Two modalities are demonstrated with two
biometric templates.
Figure 1: Overview of methodology for cancellable biometrics [3]
Void and iris datasets are used to make experiments. For transformations, three techniques are
used namely convolution, interpolation, and bio-hashing. When compared, the interpolation has
proved to be more accurate. Overall performance is improved when Sum or SVM techniques are
used in all the cases analysed. With different fusion approaches, multi-biometric cancellable
recognition was achieved. The results revealed that using multiple transformations can improve
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [83]
the robustness of the cancellable biometrics approach. In both the template cases, the cancellable
transformations are generated and then multiple individual classifiers are generated. Finally
multi-algorithm and multi-modal fusion is used for final transformation [3]. There is a study [20]
on cancellable multi-biometrics based on adaptive bloom filters and iris codes.
3. Biometric Binary Strings
Hamming Distance Classifier (HDC) for predicting False
Rejection Rate (FRR) and False Acceptance Rate (FAR)
Based on the hamming distance threshold was proposed [4]. The proposed approach can be used
in the real world biometric modalities such as face, signature, and iris and fingerprint texture.
Moreover, they proposed a template protected biometric authentication system.
Figure 2: Overview of template protected biometric verification system [4]
As can be seen in Figure 2, it is evident that there are two phases such as enrolment and
verification. In either case, feature extraction is made and real-valued classifier is built. There are
two important modules such as secure bit extraction and secure key binding verification. The
former module is used for transforming real-valued features into a binary string which is further
used in secure key binding verification. The latter module is meant for verification of the
protected target biometric string. Such string is bound with cryptographic key for highest level of
security [4].
4. Multi-Model Biometrics
There is studied iris and face as biometric features to protect communications in mobile devices
[6]. As the mobile devices are vulnerable to various attacks, authentication with iris and face
could prevent them. They built a mobile management system using biometrics which is
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [84]
embedded in mobile devices. This solution can also be used in security-critical applications in
the real world. Their system is named FIRME which has the architecture as presented in Figure
3.
Figure 3: Combination of iris and face for recognition [6]
As can be seen in Figure 3, it is evident that there are many phases for modelling face and iris
and fusing them for recognition. The phases include capture, detection of iris, detection of face,
segmentation of iris, segmentation of face, feature extraction of iris, anti-spoofing of face,
feature extraction of face, template selection for iris and face, matching of iris and face and
fusion. With the help of the two models and fusion, the system is able to recognize humans live.
There is a framework focused on biometric systems for mobiles using data mining techniques
and ECG based identification [23]. There is studied [8] iris and fingerprints together for human
identification. They focused on the UID project in India named “Aaadhar”. The combination of
iris and fingerprints make the system robust and can uniquely identify humans across the globe.
Figure 4: Combination of iris and fingerprint for biometric security [8]
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [85]
From Figure 4, it can be assumed that the iris of human eye and fingerprints together form a
basis for high security in human identification. The fusion of these two is challenging for feature
selection. However, many real world systems are using the combination of both. For finding
similarity in trained and testing samples two distance measures such as Mahalanobis distance
and Euclidean distance are used. Thus the identity of a person can be established.
5. GA for Iris Reconstruction
There is a framework [7] used binary iris code for reconstruction of original iris image.
Probabilistic approach was used along with genetic algorithms for iris image reconstruction from
given binary templates. This solution was proved realistic and had potential to support iris as
reliable biometric feature for human identification. This solution has three phases namely
segmentation, normalization and occlusion mask and encoding. These three phases are as
visualized in Figure 5.
Figure 5: General phases in iris recognition [7]
As can be seen in Figure 4, it is evident that the solution has three phases. In the first phase
segmentation takes place. In the second phase normalization takes place for transforming iris
segments into a rectangular image.
The encoding phase uses some sort of filtering that can for binary representation of iris image or
iris code which is further used for human authentication.
6. Security Issues with Biometric Systems
Biometric systems that make use of multiple features of biometrics have been reported to face
attacks. Though biometric technology captures what is being done and who is doing it, there are
direct and indirect attacks directed towards face and iris fusion. Recent research revealed that
multi-model biometric systems are vulnerable to spoofing attacks. There might be other software
based attacks still unexplored in the real world. According to research as in [9] spoofing attacks
are considered direct attacks that are made with synthetic biometric features or iris images that
are forged. Indirect attacks are the attacks that are made on the inner modules of the biometric
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [86]
system. They are classified into three types namely attacks to the system database, attacks to the
communication channels, and attacks on the feature extractor. They proposed an attack to break
the security of multi-model biometric system. Their attack demonstrates that the biometrics
verification system can get compromised at four stages such as segmentation, normalization and
feature encoding and matching. Their experiments proved that the software based attack was able
to reveal the vulnerabilities of the multi-model biometric system.
A research [10] presented a hypothesis “genetically undistinguishable irises have texture
similarity that is not detected by iris biometrics”. Genetically identical irises can be found with
twins and both eye irises of same person. However, the similarity between genetically identical
irises is not detected by iris biometrics. This provides more security as the biometrics is assumed
to be highly secure. Some of the challenging queries with respect to left/right human irises are as
presented in Figure 6.
Figure 6: Challenging iris queries that causes incorrect responses [10]
Though the iris pairs are matching, the responses were that they are unrelated. This is due to the
hypothesis taken by the researcher which has been proved. Sometimes it is possible to depend on
human experts when iris technology is unable answer correctly [10]. In similar lines a sin [19]
made experiments on the hypothesis that “texture has effects on iris recognition”. Their
experiments proved that over a period of time iris recognition failure is attributed to the effects of
texture and found the need for dealing with texture.
7. Iris Localization in Frontal Eye Images
Iris localization is very important activity in commercial iris recognition systems. However, the
validation of method s is limited to laboratory data and not for realistic data. A research [11]
proposed an algorithm that proved to be robust with not ideal data which is less constrained. It
has operations like localizing outer and inner boundaries of iris, and the process of suppressing
specular reflections. It also has regularization of circular boundaries. The results of this research
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [87]
reveal that the algorithm is robust in presence of eyelids occlusions, eyelashes, hair, contact lens
and glasses. The overview of the algorithm is as presented in Figure 7.
Figure 7: Overview of the algorithm [11]
As can be viewed in Figure 7, it is evident that the given eye image is subjected to preprocessing
before applying two phases of iris localization and finally dealing with non-circular boundaries.
The experiments conclude that specular reflections very useful in iris recognition, two phase
strategy is robust, circular Hough transform can withstand and deal with broken contours, active
contours and radial gradients can be used for regularizing inner and outer iris contours [11].
Research in similar lines focusing on gray level intensity [12], radial-gradient operator, and
Hough transform for iris localization. In similar fashion, research [13] explored gray level
statistics and image projection function for iris recognition. Yet in another experiment a s i n [14]
used Hough transform, eccentricity and histogram-bisection for iris localization purposes. In
another significant research activity as in [15] focused on non-ideal data for non-circular iris
localization by proposing a new localization technique.
8. Other Approaches to Iris Recognition
Research as in [5] focused ocular biometrics including iris recognition. Ocular biometrics
became popular as they are proved to be secure biometric features. They focused on the sclera
texture and vasculature patterns for biometric authentication to form an ocular-based recognition
system. Biometrics is the science of identifying people based on their behavioural or physical
traits such as face, iris, fingerprints and voice. As in paper [17] combined error correction codes
and finger prints in order to build an effective biometric system. There is a study [18] focused on
a multi-model biometric system using face and iris combination. SVM and feature selection
techniques were used in the recognition process. A research [21] proposed an algorithm for iris
segmentation using Circular Hough Transform and K-Means algorithm. The experiments were
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [88]
made on iris recognition in unconstrained environments. Similar study was made as in [22] using
combination of different approaches for iris recognition. The following Table presents various
statistical comparisons of biometric techniques.
Table 1: various biometric techniques
Technique/coded/pattern
Misidentification rate
Security/applications
Iris recognition Iris pattern
1/1200000
High, high security zone
Finger printing
1/1000
Medium, universe
Hand figure size, length, shape of hand
1/700
Low, less safety zone
Facial recognition Outline, shape and
distribution of eyes and nose
1/100
Low, less safety zone
Signature shape of letters, writing
order, pempressure
1/100
Low, less safety zone
Voice printing voice characteristics
1/30
Low telephonic services
The various technical issues involved in the recognition of iris can be subdivided into four parts.
The first set of issues includes image acquisition. The second step includes segmentation of the
iris from the iris image. The third part concerns with feature extraction from the segmented iris
image. Finally the fourth part deals with the matching algorithms to match the iris pattern.
9. Conclusions and Future Work
In this paper, our focus is on iris as biometrics feature for secure authentication and identification
of humans uniquely across the globe. Iris is one of the physiological biometric features which are
regarded as highly reliable in biometric identification systems. It is used in multimodal
biometrics and in combination with cryptography. It is also considered to be most inequitable of
facial biometrics. However, it is found that iris localization in influenced by texture. When it is
not interpreted properly, commercial iris-based biometric systems provide inaccurate results
while identifying humans. Moreover, it is important that iris-based identification systems should
work with both ideal and non-ideal iris images otherwise the security will be at stake. This study
revealed that iris-based biometric systems tend to provide false results in non-cooperative
environments. Another important insight is that iris can be used in mobile communications with
smart devices. Cancellable biometrics is useful for robust security in the presence of attacks.
There are direct and indirect attacks on multimodal biometrics that need to be overcome. Further
research is required in order to see that such attacks cannot break security of systems which are
based on biometrics. With these insights in mind, in future, we focus on ATM terminal design
using iris recognition in banking domain.
References
[1] R. Álvarez Mariño, F. Hernández Álvarez, L. Hernández Encinas. (2012). A crypto-biometric
scheme based on iris-templates with fuzzy extractorse. Information Sciences, 195, p91-102.
[2] GauravBhatnagar, Jonathan Wua, Balasubramanian Ramanb. (2012). Fractional dual tree
complex wavelet transform and its application to biometric security during communication and
transmission. Future generation computer systems, 28, 1, 2012, p254-267.
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [89]
[3] Anne M.P. Canuto, Fernando Pintro, João C. Xavier-Junior. (2013). Investigating fusion
approaches in multi-biometric cancellable recognition. Expert Systems with applications, 40, 6,
p1971-1980.
[4] C.Chen and R.Veldhuis. (2011). Extracting biometric binary strings with minimal area under the
FRR curve for the hamming distance classifier. Signal processing, 91, 4, p906-918.
[5] Simona Crihalmeanu and Arun Ross. (2012). Multispectral scleral patterns for ocular biometric
recognition. Pattern Recognition Letters, 33 (1), p1860-1869.
[6] Maria De Marsico, Chiara Galdi, Michele Nappi, Daniel Riccioc. (2014). FIRME: Face and Iris
Recognition for Mobile Engagement. Image and Vision Computing. P1161-1172.
[7] Javier Galbally, Arun Ross, Marta Gomez-Barrero, Julian Fierrez, Javier Ortega-Garcia. (2013).
Iris image reconstruction from binary templates: An efficient probabilistic approach based on
genetic algorithms. Computer Vision and Image Understanding, 117 (1), p1512-1525.
[8] Ujwalla Gawande, Mukesh Zaveri, Avichal Kapur. (2013). bimodal biometric system: feature
level fusion of iris and fingerprint. Biometric Technology Today, p7-8.
[9] Marta Gomez-Barrero, Javier Galbally, Julian Fierrez. (2014). Efficient software attack to
multimodal biometric systems and its application to face and iris fusion. Pattern Recognition
Letters, 36 (1), p243-253.
[10] Karen Hollingsworth, Kevin W. Bowyer, Stephen Lagree, Samuel P. Fenker, Patrick J. Flynn.
(2011). genetically identical irises have texture similarity that is not detected by iris biometrics.
Vision and Image Understanding, 115 (1), p1493-1502.
[11] Farmanullah Jan, Imran Usman, Shahrukh Agha. (2012). Iris localization in frontal eye images
for less constrained iris recognition systems. Digital Signal Processing, 22 (1), p971-986.
[12] Farmanullah Jana, Imran Usman, Shahid A. Khana, Shahzad A. MalikaaDepartment. (2013). Iris
localization based on the Hough transform, a radial-gradientoperator, and the gray-level intensity.
Optik - International Journal for Light and Electron Optics, 124 (1), p5976-5985.
[13] Farmanullah Jan, Imran Usman, Shahrukh Agha. (2013). A non-circular iris localization
algorithm using image projection function and gray level statistics. Optik - International -241.
[14] Farmanullah Jan, ImranUsman,ShahrukhAgha (2013).Reliable iris localization using Hough
transform, histogram-bisection, and eccentricity. Signal Processing, 93 (1), p230
[15] Farmanullah Jan, Imran Usman, Shahid A. Khan, Shahzad A. Malik. (2014). A dynamic non-
circular iris localization technique for non-ideal data. Computers & Electrical Engineering, p215-
226.
[16] Yooyoung Lee, James J. Filliben, Ross J. Micheals, P. Jonathon Phillips. (2013). Sensitivity
analysis for biometric systems: A methodology based on orthogonal experiment designs.
Computer Vision and Image Understanding, 117 (1), p532-550
[17] Peng Li, Xin Yang, Hua Qia, Kai Cao, Eryun Liu, Jie Tian. (2012). an effective biometric
cryptosystem combining fingerprints with error correction codes. Expert Systems with
Applications, 39 (1), p6562-6574
[18] Heng Fui Liau and Dino Isa. (2011). Feature selection for support vector machine-based face-iris
multimodal biometric system. Expert Systems with Applications. 38 (1), p11105-11111.
[19] D.M. Rankin, B.W.Scotney, P.J.Morrow a, B.K.Pierscionek. (2012). Iris recognition failure over
time: The effects of texture. Pattern Recognition, 45 (1), p145-150.
[20] C. Rathgeb and C. Busch. (2014). Cancelable multi-biometrics: Mixing iris-codes based on
adaptive bloom filters. Computers & Security, 42 (1), p1-12.
[21] Shaaban A.Sahmoud, IbrahimS.Abuhaiba. (2013). Efficient iris segmentation method
in unconstrained environments. Pattern Recognition, 46 (1), p3174-3185.
[22] Gil Santos and Edmundo Hoyle. (2012). A fusion approach to unconstrained iris recognition.
Pattern Recognition Letters, 33 (1), p984-990.
[23] Khairul Azami Sidek, Vu Mai, Ibrahim Khalil. (2014). Data miningin mobile ECG
basedbiometric identification. Journal of Network and Computer Applications. 44 (1), p83-91.
[Reddy et. al., Vol.6 (Iss.1): January, 2018] ISSN- 2350-0530(O), ISSN- 2394-3629(P)
(Received: Jan 11, 2018 - Accepted: Jan 23, 2018) DOI: 10.5281/zenodo.1162210
Http://www.granthaalayah.com ©International Journal of Research - GRANTHAALAYAH [90]
[24] R. Szewczyk, K.Ggrabowski, M. Napieralska, W. Sankowski, M. Zubert, A. Napieralski. (2012).
A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern
Recognition Latyters. 33(1), p1019-1026.
[25] B.Thiyaneswaran, R.Kandiban and Dr. K.S. JayaKumar. (2012). Elimination of IRIS hazards
intended for localization using visible features of iris region. Procedia Engineering. 38(1), p246-
252.
[26] J.A.Unar, WooChawSeng, AlmasAbbasi. (2014). A review of biometric technology along with
trends and prospects. Pattern Recognition. 47(1), p2673-2688.
[27] Heguihu, Cheng Zhao, Xiangde Zhang, Lianping Yang. (2013). A novel iris and chaos-based
random number generator. Computers & Security. 36(1), p40-48.
[28] Jin Ok Kim, Woongjae Lee, Jun Hwang, Kyong Seok Baik, Chin Hyun Chung, Lip print
recognition for security systems by multi-resolution architecture, Future Gener. Comput. Syst. 20
(2) (2004) 295-301.
[29] D. Maltoni, D.Maio. A.k. Jain, S. Prabhakar. Handbook of Fingerprint Recognition, Springer
Verag, Berlin, Germany, 2003.
[30] Maltoni, D.Maio. A.k. Jain, S. Prabhakar. (2009). Handbook of Fingerprint Recognition, (2nd
Ed.). Springer publishing Company, Incorporated.
*Corresponding author.
E-mail address: bramhareddy999@ gmail.com
Article
Iris localization plays a decisive role in the overall iris biometric system’s performance, because it isolates the valid part of iris. This study proposes a reliable iris localization technique. It includes the following. First, it extracts the iris inner contour within a sliding-window in an eye image using a multi-valued adaptive threshold and the two-dimensional (2D) properties of binary objects. Then, it localizes the iris outer contour using an edge-detecting operator in a sub image centered at the pupil center. Finally, it regularizes the iris contours to compensate for their non-circular structure. The proposed technique is tested on the following public iris databases: CASA V1.0, CASIA-Iris-Lamp, IITD V1.0, and the MMU V1.0. The experimental and accuracy results of the proposed scheme compared with other state-of-the-art techniques endorse its satisfactory performance.
Article
This paper investigates the robustness of performing biometric identification in a mobile environment using electrocardiogram (ECG) signals. We implemented our proposed biometric sample extraction technique to test the usability across classifiers. Subjects in MIT-BIH Normal Sinus Rhythm Database (NSRDB) were used to validate the reliability and stability of the subject recognition methods. Discriminatory features extracted from the experimentations were later applied to different classifiers for performance measures based on the complexity of our proposed sample extraction method when compared to other related algorithms, the total execution time (TET) applied on different classifiers in various mobile devices and the classification accuracies when applied to various classification techniques. Experimentation results showed that our method simplifies biometric identification process by obtaining reduced computational complexity when compared to other related algorithms. This is evident when TET values were significantly low on mobile devices as compared to a non-mobile device while maintaining high accuracy rates ranging from 98.30% to 99.07% in different classifiers. Therefore, these outcomes support the usability of ECG based biometric identification in a mobile environment.
Article
Identity management through biometrics offer potential advantages over knowledge and possession based methods. A wide variety of biometric modalities have been tested so far but several factors paralyze the accuracy of mono-modal biometric systems. Usually, the analysis of multiple modalities offers better accuracy. An extensive review of biometric technology is presented here. Besides the mono-modal systems, the article also discusses multi-modal biometric systems along with their architecture and information fusion levels. The paper along with the exemplary evidences highlights the potential for biometric technology, market value and prospects.
Article
The iris biometric recognizes a human based on his/her iris texture, which is a stable and unique feature for every individual. A typical iris biometric system performs better for the ideal data, which is acquired under controlled conditions. However, its performance degrades when localizing iris in non-ideal data containing the noisy issues, e.g., the non-uniform illumination, defocus, and non-circular iris boundaries. This study proposes a reliable algorithm to localize iris in such images robustly. First, a small region containing the coarse location of iris is localized. Next, the pupillary boundary is extracted within this small region using an iterative-scheme comprising an adaptive binarization and a pupil location verification test. Following that, the limbic boundary is localized by reusing the Hough accumulator. The iris location is also verified through a gray-level test. After that, the pupillary and limbic boundaries are regularized by applying an enhanced method comprising a Radial-gradient operator (RGO), an error-transform (ET), and the Fourier series. Experimental results, obtained on the CASIA-IrisV3, CASIA-IrisV4, MMU V1.0, and MMU(new) V2.0 iris databases, show superiority of the proposed technique over some of the contemporary techniques. (C) 2013 Published by Elsevier GmbH.
Article
Iris recognition technology identifies an individual from its iris texture with great precision. A typical iris recognition system comprises eye image acquisition, iris segmentation, feature extraction, and matching. However, the system precision greatly depends on accurate iris localization in the segmentation module. In this paper, we propose a reliable iris localization algorithm. First, we locate a coarse eye location in an eye image using integral projection function (IPF). Next, we localize the pupillary boundary in a sub image using a reliable technique based on the histogram-bisection, image statistics, eccentricity, and object geometry. After that, we localize the limbic boundary using a robust scheme based on the radial gradients and an error distance transform. Finally, we regularize the actual iris boundaries using active contours. The proposed algorithm is tested on public iris databases: MMU V1.0, CASIA-IrisV1, and the CASIA-IrisV3-Lamp. Experimental results demonstrate superiority of the proposed algorithm over some of the contemporary techniques.
Article
In this work adaptive Bloom filter-based transforms are applied in order to mix binary iris biometric templates at feature level, where iris-codes are obtained from both eyes of a single subject. The irreversible mixing transform, which generates alignment-free templates, obscures information present in different iris-codes. In addition, the transform is parameterized in order to achieve unlinkability, implementing cancelable multi-biometrics. Experiments which are carried out on the IITD Iris Database version 1.0 confirm the soundness of the proposed approach, (1) maintaining biometric performance at equal error rates below 0.5% for different feature extraction methods and fusion scenarios and (2) achieving a compression of mixed templates down to 10% of original size.
Article
Mobile devices, namely phones and tablets, have long gone “smart”. Their growing use is both cause and effect of their technological advancement. Among the others, their increasing ability to store and exchange sensitive information, has caused interest in exploiting their vulnerabilities, and the opposite need to protect users and their data through secure protocols for access and identification on mobile platforms. Face and iris recognition are especially attractive, since they are sufficiently reliable, and just require the webcam normally equipping the involved devices. On the contrary, the alternative use of fingerprints requires a dedicated sensor. Moreover, some kinds of biometrics lend themselves to uses that go beyond security. Ambient intelligence services bound to the recognition of a user, as well as social applications, such as automatic photo tagging on social networks, can especially exploit face recognition. This paper describes FIRME (Face and Iris Recognition for Mobile Engagement) as a biometric application based on a multimodal recognition of face and iris, which is designed to be embedded in mobile devices. Both design and implementation of FIRME rely on a modular architecture, whose workflow includes separate and replaceable packages. The starting one handles image acquisition. From this point, different branches perform detection, segmentation, feature extraction, and matching for face and iris separately. As for face, an antispoofing step is also performed after segmentation. Finally, results from the two branches are fused. In order to address also security-critical applications, FIRME can perform continuous reidentification and best sample selection. To further address possibly limited resources of mobile devices, all algorithms are optimized to be low-demanding and computation-light.
Article
Recently, iris recognition systems have gained increased attention especially in non-cooperative environments. One of the crucial steps in the iris recognition system is the iris segmentation because it significantly affects the accuracy of the feature extraction and iris matching steps. Traditional iris segmentation methods provide excellent results when iris images are captured using near infrared cameras under ideal imaging conditions, but the accuracy of these algorithms significantly decreases when the iris images are taken in visible wavelength under non-ideal imaging conditions. In this paper, a new algorithm is proposed to segments iris images captured in visible wavelength under unconstrained environments. The proposed algorithm reduces the error percentage even in the presence of types of noise include iris obstructions and specular reflection. The proposed algorithm starts with determining the expected region of the iris using the K-means clustering algorithm. The Circular Hough Transform (CHT) is then employed in order to estimate the iris radius and center. A new efficient algorithm is developed to detect and isolate the upper eyelids. Finally, the non-iris regions are removed. Results of applying the proposed algorithm on UBIRIS iris image databases demonstrate that it improves the segmentation accuracy and time.