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Iris-Pupil Thickness Based Method for Determining Age Group of a Person

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Iris-Pupil Thickness Based Method for Determining Age Group of a Person The International Arab Journal of Information Technology, Vol. 13, No. 6, November 2016 715 Iris-Pupil Thickness Based Method for Determining Age Group of a Person Asima Abbasi and Muhammad Khan Shaheed Zulfikar Ali Bhutto Institute of Sciences and Technology, Pakistan Abstract: Soft biometric attributes such as gender, ethnicity and age can be determined from the iris images. Pupil size plays an important factor in iris template aging. In this study, statistical experiments are performed to find out confidence interval for Iris-Pupil thickness of different age groups such as children, youth and senior citizen. Significant group differences have been observed by applying statistical techniques such as Analysis of Variance (ANOVA) and the Tukey’s pairwise comparison test. The results of the study conclude that the proposed methodology can be employed to determine age group of a person from the available iris images. Based on the study results, we argue that performance of an iris recognition system can be enhanced by identifying age group of the persons from their iris images. Keywords: Iris recognition, feature extraction, iris aging, iris pupil ratio. Received July 10, 2014; accepted April 2, 2015; Published online December 23, 2015 1. Introduction Ptosis (blepharoptosis): Drooping of eyelids occurs Substantial efforts spanning over a long period have during the old age. The drooping eyelid can cover been made by the research community towards all or part of the pupil and results in interference with the vision. Hence, the Euclidian distance guessing approximate age of a human face. Presently, an interesting topic in iris biometric is to resolve or between upper and lower eyelid would be lesser in the elderly people as compared to the children and determine age from the iris image. Studies show that with the passage of time, enrolled iris images results in youth. an increase in false non-matching rate [6]. Still Corneal Shape: Fenker et al. [8] state that at sufficient research has not been reported that can help younger age, cornea tends to have greater curvature decide age bracket of a person from the available iris along the horizontal axis than the vertical axis. Also, image. Age estimation based on snapshot of the eye is the distance from corneal surface to the iris also relatively more difficult because the tempo at which changes during the younger age. Eye wrinkles and structure/characteristics of human eye are changed is skin sags result in dull eye color. Due to this, less not very well known. In the establishment of iris sclera is visible in old age and eyes do not remain as biometric research, the research community has round, open and wider as it is used to be in the somewhat compromised with the earlier findings that younger people. iris remains stable throughout the life of a person. Arcus Senilis: A white or grey arc/ring appears in “Template aging” and “Iris aging” are two elderly adults around the outer part of the cornea. dissimilar terms, as template aging takes place when People living in different environments can have enrolled image and verified image match score different iris ageing factors which needs to be degrades after lapse of certain time, but with iris aging further investigated to decide upon choosing it as a it means that considerable changes can occur in iris candidate attribute for iris recognition research [2, texture pattern as humans grow older [6]. It is 3]. generally emphasized that significant features of an iris The rest of this paper is organized as follows: Section 2 do not change and remain stable for many years [9], provides a related work, an overview of template aging but there are some other factors that occur in the eye and image dataset. Section 3 describes the problem structure in old age. Some of these important factors statement and the research methodology adopted for include: conducting this research. Section 4 presents the Pupil Size: Decreases linearly in healthy adults [12]. experimental results and details of the statistical Pupil dilation decreases with age and consequently analysis performed on the datasets. Finally, sections 5 hamming distance among the pupil sizes of a person and 6 summarize the discussion and conclusion obtained at different stages of life slightly decreases respectively. [5]. In this regard, younger people exhibit greater pupil dilation than elderly people. 716 The International Arab Journal of Information Technology, Vol. 13, No. 6, November 2016 2. Related Works long historical image. This is the only worldwide database with such a long time distance between Fenker and Bowyer [6] used dataset having three year captured sessions. The authors evaluated short term span between acquisition and verification of images interval to two years versus long term assessment from based on four matchers i.e., Iris Bee, VeriEye and two five to nine years and concluded that short term commercial matcher. For this purpose, 285 million comparisons demonstrated improved match. comparisons were investigated because of all-vs-all Abbasi et al. [1] critically evaluated iris biometric experiments with each matcher. VeriEye proved to be identification and verification methods. The authors the best among four matchers. Fenker and Bowyer [6] notified that accuracy and performance can be took small time lapse match between two images achieved by eliminating inferiority images and using which belonged to the same year and long time lapse only the quality images and discussed that iris match between images belonging to different years. segmentation is a challenging task for off angle images Ortiz et al. [12] propose a linear regression model to especially noisy and blur images. Iris can be divided explore pupil dilation in terms of age and match scores. into numerous regions and verification of single region VeriEye and IrisBee software are used to produce can identify individuals. Iris biometrics can be applied match data. The authors then compare results of to identify soft biometric attributes of an individual medical literature to determine how pupil size changes such as gender, ethnicity and age group. with age and conclude that measureable effect in pupil Ziauddin and Dailey [15] proposed a hybrid dilation has been observed due to age for actual match technique to localize pupil and iris region. Pupil assessment. segmentation is done by first establishing an intensity Fairhurst and Erbilek [5] investigate the effect of value by acquiring most of the pupil region, and then a physical ageing on performance of iris recognition point inside the pupil is calculated to get its radius. The system by analyzing multiple aspects such as image radius point is moved in all the four directions to get quality level, improved segmentation algorithm, degree background pixels and estimate the final radius. For of pupil dilation and dividing age groups into different iris segmentation, relevant region is smoothed by categories. Quality levels are achieved by dividing Gaussian filter first and then vertical edges are available sample images into three groups: Good, poor obtained by performing canny edge map. Horizontal and bad. Good images are properly segmented, poor edges are ignored as these edges mostly contain noise and bad images are imperfectly segmented due to noise due to eyelid and eyelashes. Lastly, the circular Hough and small iris region. transform is applied to get the final iris circle. In [11], Fenker et al. [8] discussed methods to eliminate biometric data manipulation techniques have also been template aging and proposed that multiple iris images explored for intrinsic authentication of multimedia should be stored with different dilation value means. objects. A critical review of the template aging Another way is to utilize those sensors that control techniques reported in the contemporary literature is dilation value. To reduce template aging both aspects shown in Table 1. (i.e., algorithm for matching templates and sensor use for image acquisition) should be focused. Sgroi et al. Table 1. Summary of template ageing techniques [14] investigated a classification technique which Ref Area Focus Technique Used Strength Weakness Used linear regression Analyzed behavior of Focused single Template ageing categorizes person as younger or older with the help of model to analysis pupil the match score and factor, focusing on [12] based on pupil dilation with age as well as variation in pupil various factors can iris texture. Lagree and Bowyer [10] used texture dilation. dilation difference. dilation. give more insight Divided age into three feature that are similar to those used by the researchers Experiment groups less than 25, greater Proposed segmentation pupil dilation Investigate effect on than 25 and less than 60, algorithm achieves for prediction of gender and ethnicity. Proposed work [5] effect on short time lapse (2 greater than 60. Improved accuracy 99.53 on bio performance of years). provides the preliminary study in age prediction from segmentation algorithm to secure database. system. analyze physical ageing iris texture images. Discuss several factors Approaches contributing eye Fenker and Bowyer [7] report that average pupil controlling Three year time lapse [8] ageing, found FNMR - template ageing dataset. increases with long dilation changes over time which ultimately results in are explained. time lapse. Dataset evenly split for changing the iris texture; and as an enrollment time Old and young younger and orderly group. Old and young age age group Less accuracy level passes, it increases non false match. Proposed work [14] Random forest algorithm group is identified identification achieved. has been used. with accuracy 64%. about template aging is different from other from Iris texture Six filters are used to create contemporary research as it uses large datasets. Also, Ethnicity and feature vector. Mainly are Achieved 62 % No considerable gender spot and line detector, thick accuracy in gender dissimilarity pupil dilation and contact lenses factors have been [10] prediction based horizontal line, thin prediction with mixed achieved with single handled by creating data subsets and the same is tested on iris texture.
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