Trade off between variable and fixed size normalization in orthogonal polynomials based iris recognition system
 R. Krishnamoorthi^{1}Email author and
 G. Anna Poorani^{1}
https://doi.org/10.1186/s400640161909y
© Krishnamoorthi and Anna Poorani. 2016
Received: 7 December 2015
Accepted: 17 February 2016
Published: 24 March 2016
Abstract
Iris normalization is an important stage in any iris biometric, as it has a propensity to trim down the consequences of iris distortion. To indemnify the variation in size of the iris owing to the action of stretching or enlarging the pupil in iris acquisition process and camera to eyeball distance, two normalization schemes has been proposed in this work. In the first method, the iris region of interest is normalized by converting the iris into the variable size rectangular model in order to avoid the under samples near the limbus border. In the second method, the iris region of interest is normalized by converting the iris region into a fixed size rectangular model in order to avoid the dimensional discrepancies between the eye images. The performance of the proposed normalization methods is evaluated with orthogonal polynomials based iris recognition in terms of FAR, FRR, GAR, CRR and EER.
Keywords
Pupil border Limbus border Variable size normalization Fixed size normalizationBackground
Owing to differences in the image capturing distance and in the illuminationed environment, the size of the subject’s pupil and the iris region of interest in the captured images are highly diverging. An accumulation of this, there is discrepancies of the same eye images owing to stretches of the iris. Additional criteria that cause dilation are eye rotary motion, revolving camera and head inclination. Such a deformation of the iris texture broadens intraclass dissimilarities and raises the FRR.
A survey of the existing normalization techniques found in the literature is presented in the following section.
Review of literature
From the perspective of iris texture feature extraction, the normalization techniques are classified into six categories such as Linear model (Daugman 1993; Lim et al. 2001; Joung et al. 2005; Boles and Boashash 1998; Ma et al. 2003; Subbarayudu and Prasad 2008; Shamsi and Rasouli 2009, 2011), variant of linear model (Krishnamoorthi et al. 2012), nonlinear model (Wildes 1997; Wyatt 2000), the combination of nonlinear and linearmodels (Wei et al. 2007; Yuan and Shi 2005), nonpolar coordinate normalization (Arvachech and Tizhoosh 2006) and irregular border normalization (Han et al. 2009; Shah and Ross 2009).
Daugman (1993) employed linear rubber sheet model which projects the doughnut shaped iris region of interest into a fixed rectangle region. Lim et al. (2001) have used a fixed resolution model very similar to the Daugman’s pseudo polar transform approach. They have normalized the distance between the pupil border and the limbus border into [0, 60] according to the arbitrary radius r and normalized the angular resolution into [0, 450] according to the step angle 0.8°. Joung et al. (2005) have unwrapped the iris with limbus center to define the polar coordinates of the points over the limbus border and used pupil center to define the polar coordinates of the pupil border. The coordinates of the other points between these two borders are obtained linearly in the radial direction. Boles and Boashash’s normalization technique (Boles and Boashash 1998) is also similar to Daugman’s method with the difference that it is performed at the time of matching. Their method is based on the virtual circles to map the iris features. Ma et al. (2003) have combined the Daugman’s method (Daugman 1993) and Bole’s method (Boles and Boashash 1998). They have used the pupil center as a reference point in their mapping strategy. Subbarayudu and Prasad (2008) have assumed that the pupil and limbus boundaries are two circles and utilized angular strips radial measure to map iris region. Shamsi and Rasouli (2009) have devised a new mapping strategy to rescale point. Shamsi and Rasouli (2011) have transformed iris disk to trapezium strip.
Krishnamoorthi et al. (2012) have devised a variation of trapezoidal model to avoid the under samples near the limbus border. Wildes (1997) has reported an image registration technique for compensating variations in rotation and scale. Wyatts et al. (2000) have used the virtual arc concept and carried out the mapping from the reference annular zone into a fixedsize rectangle zone. Wei et al. (2007) have utilized Gaussian function to estimate the additive variation of a nonlinear iris stretch. Yuan and Shi (2005) have considered the nonlinear behavior of iris patterns with a predefined ratio of the radii of the pupil and limbus boundaries of the iris. Arvachech and Tizhoosh (2006) have merged the nonlinear model and linear model to unwrap an iris region of interest properly. Han et al. (2009) have designed a normalization method that does not adopt the polar coordinate transformation. They have preserved the original geometric structure and directional information. Shah and Ross (2009) have formulated the normalization technique for conical iris boundaries.
Motivated by the fact that iris boundaries are not in specific shapes, variablesize and fixedsize iris normalization techniques are proposed in this work for normalizing the irregular iris boundaries.

Estimation of the center and radius of pupil

Estimation of the coarse radius of limbus

Estimation of the accurate radius of the limbus

Computation of the resolution angle of increment and

Identification of the sampling points.
Preprocessing
Initially, the coarse estimation of pupil center is found as the point that corresponds to local minima of image intensity. The extraction of coarse pupil localization area on four sides from the coarse pupil center is modeled with approximation of pupil center and radius to confine the search for the pupil border. Then, an edge image is generated by applying negatively sloped zerocrossing point with orthogonal polynomials (Ganesan and Bhattacharya 1997). The fine pupil boundaries are then extracted after detecting radial border points in the angular direction of the projection curve. The pupil border points are fitted using the cubic smoothing spline.
The limbus border extraction is then carried out with gradient based edge detection on the same orthogonal polynomials model. Initially, the coarse limbus region is estimated with approximation of pupil center and radius to confine the search for the limbus border. This coarse limbus region is subjected to the orthogonal polynomials and after that the precise limbus border points are extracted with vertical and horizontal edge detection. The limbus curvature is approximated with cubic smoothing spline from the limbus border points.
Proposed variablesize normalization model
By increasing the angle θ by \(\phi\) for radius r _{ l }, the variablesize rectangular resolution iris image for the plane (θ, r _{ l }) is obtained. Also the degree of rotation (360°) is calibrated in such a way that it reaches each position in the limbus border.
In this way, points from limbus border to pupil border for each position are traced and stored in angular resolution array with step \(\phi\) times until θ becomes 360°.
Proposed fixedsize normalization model
In this way, points from pupil border to limbus border for each position are traced and stored in angular resolution array step \(\phi\) times until θ becomes 360°.
Orthogonal polynomials based iris recognition
With a view to extract iris texture feature, the normalized iris is further subjected to the orthogonal polynomials to extract the transformed coefficients (Ganesan and Bhattacharya 1997). The variance is computed from the transformed coefficients and the sets such as main effects, interaction effects are obtained (Krishnamoorthi and Kannan 2009). The spatial variation that causes the interaction effects are owing to micro texture present in the iris region. To investigate whether a specified region possesses texture characteristics, the Hartley’s criteria are applied for testing the homogeneity amongst variances (Krishnamoorthi and Anna Poorani 2012). Once, texture regions are identified, the Fratio test is applied for computing the SNR and the result of the Fratio test for determining significance towards the micro texture is encoded as a binary string. The corresponding decimal numeral is found subsequently to characterise the micro texture (Krishnamoorthi et al. 2013). The numerical characterization sequence is used as feature vector for further processing in iris recognition.
The dimension of features in feature vector is reduced by means of LDA (Liu and Xie 2006). It is employed to discard the null space of between_class_scatter S _{ b }, by first diagonalizing between_class_scatter S _{ b } and then diagonalizing within_class_scatter S _{ w }. The support vectors of the query image are computed to match the query image with the database images from the reduced feature vector using Nonlinear asymmetrical support vector machine (SVM) matching scheme (Roy and Bhattacharya 2006).
Empirical results and discussion
Computation time for normalization process, with the proposed scheme on BITIRIS database images
Method used  Time taken for normalization (s) 

Proposed variable size normalization model  2.234 
Proposed fixed size normalization model  1.586 
Outcomes of proposed iris normalization schemes on BITIRIS database images in terms of FAR, FRR and CRR
Method  BITIRIS database  

FAR (%)  FRR (%)  CRR (%)  
Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.010  0.112  99.88 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.015  0.165  99.82 
It is clear from Table 2, that the proposed variablesize normalization scheme outperforms the proposed fixedsize normalization scheme.
It is exemplified from the Fig. 7 that the proposed variable size iris normalization scheme attains higher GAR with an extremely low EER than fixed size iris normalization scheme on the BITIRIS database.
Outcomes of proposed iris normalization schemes on BITIRIS database images in terms of EER
Method  EER (%) 

Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.100 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.145 
It is evident from Table 3 that the EER of the variable size iris normalization scheme is found to be superior than fixed size iris normalization scheme. It is also wellknown from Table 3 that the proposed variable size normalization scheme is able to attain close proximity to zero EER. Extremely low EER of 0.100 % reveals the robustness of the variable size iris normalization scheme in verification mode.
Outcomes of proposed iris normalization schemes on various iris database images in terms of FAR, FRR and CRR
Iris data base  Method  FAR (%)  FRR (%)  CRR (%) 

CASIA V 1.0  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.011  0.354  99.635 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.013  0.523  99.464  
CASIA V 3.0 Interval  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.021  0.312  99.667 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.025  0.399  99.576  
BATH  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.032  0.343  99.625 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.037  0.409  99.554  
MMU V 1.0  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.019  0.156  99.825 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.021  0.171  99.808  
MMU V 2.0  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.031  0.16  99.809 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.032  0.171  99.797 
Outcomes of proposed iris normalization schemes on various iris database images in terms of EER
Iris database  Method  EER (%) 

CASIA V 1.0  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.591 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.651  
CASIA V 3.0 Interval  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  1.032 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  1.151  
BATH  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  1.014 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  1.214  
MMU V 1.0  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.156 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.161  
MMU V 2.0  Proposed variable size normalization model + orthogonal polynomials based iris recognition system  0.0007 
Proposed fixed size normalization model + orthogonal polynomials based iris recognition system  0.0009 
Conclusion
In this paper, two different normalization methods are proposed that compensate the change in size of the iris due to the action of stretching or enlarging the pupil in iris acquisition process and camera to eyeball distance. In the first method, the variable dimension is used for irregular iris images to avoid under the samples near the limbus border. In the second method, the fixed dimension is used for irregular iris images with a rectangular model to circumvent the dimensional discrepancies among the iris images. The proposed normalization methods are compared along with the orthogonal polynomials based iris recognition and analyzed to enhance the normalization stage. The impacts of proposed variablesize normalization versus rectangular normalization on extracted features are presented. From the empirical outcomes, it is examined that the variablesize normalization scheme performs better than the fixedsize normalization approach in terms of matching. It is concluded that the proposed variablesize normalization makes orthogonal polynomials based iris recognition system more robust to the illumination variations than the proposed fixedsize normalization model.
Declarations
Authors’ contributions
GAP conducted the iris normalization studies, proposed variablesize and fixedsize iris normalization techniques and drafted the manuscript. RK carried out the iris texture feature extraction based on orthogonal polynomials. GAP took part in the feature reduction and matching work. RK devised the study, and contributed in its design and coordination and facilitated to draft the manuscript. Both authors read and approved the final manuscript.
Acknowledgements
This task is fully funded by the Grant from the DIT of MIT, New Delhi under Fund No. 12(12)/2008ESD.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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