A Novel Iris Segmentation Scheme

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


Introduction
Biometric recognition is used to automatically recognize individuals based on the different and unique physiological characteristics such as face, ear, fingerprint, gait, iris, palm prints, and voice.Human's iris pattern can be used as an ideal biometric character due to its uniqueness between individuals and its fixated pattern throughout individual's life [1,2].
The iris recognition scheme consists of six steps: eyeimage capture, iris extraction, normalization, preprocessing, feature extraction, and matching [2].Accurate iris extraction is probably the most important step because it greatly influences the overall recognition accuracy and processing speed.The iris is often surrounded by noise, such as the pupil, sclera, eyelashes, and eyebrows, which will need to be removed accordingly to achieve accurate iris extraction.
In the published iris segmentation schemes, the integral differential operator and Hough transform are frequently utilized.Daugman [3] used an integral differential operator to remove the potential eyelid noise to localize the iris regions.He used the gradient ascent and radial Gaussian methods to fit the circular contours.De Martin-Roche et al. [4] applied the histogram stretch method on a grayscale eyeimage to find the three circumference parameters, center (, ), and radius  to maximize the average intensity differences of five consecutive circumferences.Circles with the detected circumference parameters are considered the iris-boundaries.Camus and Wildes [5] took a gradient-based Hough transform on the edge map of an eye-image to detect the iris-boundaries with a filtering and voting procedure.This method only performed well when the iris is obviously separated from both the pupil and the sclera.Proenc ¸a and Alexandre [6] tried four different clustering schemes to preprocess eye-images to enhance their contrast for iris segmentation.They found that the fuzzy -means clustering scheme provides the best performance when it is utilized on the position and intensity feature vector.
To preserve the advantages and overcome the disadvantages of the above-mentioned methods, a novel iris segmentation scheme is proposed.Instead of these popular edge detection schemes and circular Hough transform, the orientation matching transform (OMT) is utilized to find the outer and inner iris-boundaries and then a mathematical technique, Delogne-Kåsa circle fitting (DKCF) scheme, is used to eliminate the outlier points of the rough outer and inner iris-boundaries to extract a more precise iris area from an eye-image.Moreover, the proposed scheme utilizes the intensity and positional characteristics of the iris, eyelid, and eyelashes to obtain more accurate iris segmentation.
The performance of the proposed iris segmentation scheme is verified using an iris image database, UBIRIS.v1,created at the Soft Computing and Image Analysis Lab (SOCIA Lab) of the University of Beira Interior.This database consists of a set of visible wavelength noisy iris images, captured at close-up distance with user cooperation [7].Several error measures, such as misclassification error, relative foreground area error, accuracy, nonuniformity region, and edge mismatch are conducted to measure the performance of the proposed algorithm.The experimental results show that the proposed algorithm provides a more effective and efficient iris segmentation than other methods.The remainder of this paper is organized as follows.Section 2 illustrates the proposed iris extraction scheme.Section 3 presents the experiment results.Conclusions are given in Section 4.

Proposed Scheme
The proposed scheme is divided into three stages: (i) detecting inner and outer iris-borders, (ii) detecting eyelids, and (iii) detecting eyelashes.Figure 1 shows the flow chart of the proposed scheme.

Stage 1: Inner and Outer
Iris-Border Detecting.The flow chart of the proposed inner and outer iris-border detection is shown in Figure 2.An RGB color human eye-image is inputted and transformed from the RGB domain into the YIQ domain.Since the -component occupies 93% of the total energy in the YIQ domain, only the -component is used in the following processes to save operating time without degrading the segmentation quality.The iris in an eye-image is an annulus bounded by an inner boundary and an outer boundary.Both the inner and outer boundaries should be located before the iris extraction.Since the inner iris-boundary is the boundary between iris and pupil, to locate the inner boundary is to detect the pupil.The detailed steps for inner and outer iris-border detection are illustrated in the following subsections.

Pupil Detecting Using Outliers Detection.
In statistics an outlier is defined as an observation that deviates substantially from the other observations that it is considered generated by a different system.Outliers frequently affect the parameters estimation for finding a model fitting to the data.The outliers are frequently removed to improve the accuracy of the estimators.To define the outliers of a data set, let the mean and let  be the standard deviation  of the data set.The observation will be declared as a lower outlier if it is less than ( − ) and as an upper outlier if it is more than ( + ); the value of  is usually taken from the range between 2 and 3.
In a grayscale eye-image, the darkest areas are the pupil, eyelashes, and shadow regions.These dark regions almost always locate in the lower outliers of the pixel value distribution of a grayscale eye-image.It is therefore reasonable to take the lower outlier points of the eye-image as the candidate pupil points.Then the Canny edge filter, OMT, and DKCF are further applied on the candidate pupil regions to extract a more accurate inner iris-boundary.

Circle Detection with Orientation Matching Transform.
The Circle Hough Transform (CHT) is a known algorithm for finding given radius circular shapes within an image [8].The main disadvantages of CHT are large memory requirements and long computation time.Kimme et al. [9] modified the CHT and proposed the edge orientation based CHT (EOCHT) by utilizing the edge orientation of each element of a circle pointing toward or away from the circle's center.The EOCHT only needs to plot an arc perpendicular to the edge orientation at a distance from the edge point.This reduces the computation cost.Ceccarelli et al. [10] utilized the edge orientation characteristic of circle's elements to develop the Orientation Matching Transform (OMT) for detecting circles with radii in the range [  ,   ].It is illustrated as follows.
Let (, ) be an image and let f(, ) = ∇(, )/ |∇(, )| = ⟨cos , sin ⟩ be the orientation unit-vector of image gradient at pixel (, ), where Let      (, ) be the annulus within the image , with inner radius   , outer radius   , and center (, ) defined as where ⃗ ( − ,  − ) is the kernel vector (orientation vector of points of circles with center (, ) defined as follows: ) . ( The OMT is also used to evaluate how many points in an image have the gradient orientation coincident with the gradient orientation of circles with radii in the range [  ,   ] and uses an accumulator to find the most likely coordinates of a circle in the image.The peak in the accumulator array denotes the candidates of the circle center in the image.Once the center (, ) is decided, the points located in      (, ) are collected as the candidate points of the pupil boundary.Because iris images in UBIRIS.v1were captured at close-up distance with user cooperation, the proposed scheme utilizes the Canny edge detector on pupil-detected eye-images.It then uses OMT by setting   to 40 pixels and   to 80 pixels to find the rough inner iris-boundary.

Precise Inner Iris-Boundary Locating with Delogne-Kåsa
Circle Fitting.The points detected by OMT are tabulated in the form of ordered pairs ( 1 ,  1 ), ( 2 ,  2 ), . . ., (  ,   ).For determining a more accurate iris-boundary, a robust technique for efficiently fitting circles through noisy data, the Delogne-Kåsa circle fitting scheme, is used to locate the irisboundaries.It is illustrated as follows.
In Chan's circle functional model [11], points (  ,   ),  = 1, 2, . . ., , which are taken from the circumference of a circle, are formed as where (, ) is the center of the best circle,  is its radius,   is the phase of the th pair of measurements, and   and   represent the measurement errors that are independent and identically distributed normal random variables with zero means and standard deviations   and   , respectively.The best circle fitting is to find a circle whose circumference is as close as possible to the measured data (  ,   ),  = 1, 2, . . ..
Instead of the maximum-likelihood estimator scheme used in [11], the Delogne-Kåsa estimates scheme [12] searches the most suitable parameters , , and  so as to minimize the cost function: Mathematical Problems in Engineering By changing parameter  =  2 +  2 −  2 , it is reduced to be By taking partial derivatives with respect to , , and , respectively, the cost function is minimized when The result, can be proved to have the solution where  = ( + ),  # = (  ) where  × is the  ×  identity matrix and  × is the  ×  matrix with all elements being 1.
The proposed iris-boundary locating procedure using DKCF is given as follows.
Step 1. Set the points detected by OMT as inliers.
Step 2. Fit a circle  by applying DKCF to the inliers obtained in Step 1.
Step 3.For each point (  ,   ) of inliers, evaluate the Mahalanobis distance MD  with where  is 2 × 2 the covariance matrix,   is the mean of  component, and   is the mean of -component of inliers.
Step 4. Find the mean  MD and the standard deviation  MD of the Mahalanobis distance distribution of inliers and test all the points in inliers.If a point's Mahalanobis distance less than  MD + 1.5 MD , then reclassify it as an inlier; otherwise, reclassify it as an outlier.
Step 5. Refit circle C with Delogne-Kåsa circle fitting scheme for the updated inliers.
Step 6. Repeat Steps 3, 4, and 5 until the fitting circle does not change any further.
Once the pupil region is detected, it will be removed from the eye-image.The outer iris-boundary is a near circular boundary between the iris and sclera.In a pupil-removed grayscale eye-image, the darker regions consist of the iris and eyelashes, and the brighter regions consist of the sclera and eyelids.Hence Otsu's binarization scheme is applied on the pupil-removed eye-image to obtain the candidate iris areas.

Binarization Using
Otsu's Algorithm.The Otsu thresholding scheme, proposed by Otsu [13], first normalizes the histogram of pixels of the pupil-removed eye-image as a probability distribution and divides all pixels into two classes by a threshold.The occurrence probabilities, the means, and the variances of each class are then evaluated.And, the within-class variance, the between-class variance, and the total variance are also evaluated.The optimal threshold is evaluated using the discriminated criterion which maximizes the between-class variance and minimizes the within-class variance at the same time.In general the biggest black region in a binary eye-image obtained by the Otsu filter will contain the iris region and other smaller black regions are noises.The proposed algorithm labels these black regions and evaluates their size to extract the biggest black region as the candidate iris region.

Precise Outer Iris-Boundary Location.
The Sobel edge filter is applied on the candidate iris region to extract an initial outer iris-boundary.Then the proposed iris-boundary locating procedure using DKCF is applied to extract a more precise outer iris-boundary by setting the inner radius   to 170 pixels and the outer radius   to 200 pixels in the OMT step.Figure 3 shows the result of each step in the proposed inner and outer boundaries localization algorithm, respectively.

Stage 2: Eyelid Locating.
For a noisy iris image, the iris's upper region is frequently occluded by the upper eyelid and eyelashes either partly or completely.The lower region is usually partly occluded by the lower eyelid.Noisy iris images can cause incorrect matching and adversely affect the iris recognition rate.In order to extract the iris accurately and effectively, eyelids and eyelashes should be filtered out at high precision.
The common methods to filter out the eyelid are as follows: (i) utilize an edge detector to detect the edges of an eye-image and then apply a linear Hough transform on the eye-image edge map to locate the eyelid boundaries   and mask out the upper and lower eyelids.These linear approximations are simple but rough [14].(ii) Extract the boundary information of an eye-image and then perform the curve integral-differential operator on the eye-image edge map to locate the eyelid.These linear approximations can be significantly affected by the doublefold and eyelash roots [14].In addition, other edge points caused by eyelashes and the texture of the iris patterns may appear in the edge map.The shape of a real eyelid boundary is always not a smooth curve (or a smooth line segment).The above-mentioned schemes mentioned yield less precise eyelid detection.
To overcome the problems occurring in these schemes, an improved strategy for eyelid detection is proposed.Because eyelids occlude areas outside iris-boundaries which are not relevant, the proposed eyelid detection approach is performed only inside the iris region to increase the detection speed.After the inner and outer boundaries are located, the grayscale eye-image is segmented into three areas by two vertical lines tangent at the left and right most points in the outer boundary.The middle area is taken as the residual image and divided into seven nonoverlapped regions using six horizontal line segments: Ru, Sa, Su, Sl, Sb, and Rl as shown in Figure 4.
For detecting the upper eyelid which occludes the iris, the average grey values of a rectangular patch Ru just above the top of the iris' outer boundary and the segment Sa just below the top of the iris' outer boundary are evaluated first.The iris is treated as occluded by the upper eyelid when the absolute difference between the two average grey values is less than 10.The eyelid boundary usually appears darker than the skin and is almost always adjacent to the iris.Figures 4(b1), 4(b3), 4(b5), 4(c1), 4(c3), and 4(c5) show the column pixel value distributions with respect to the -coordinate in the upper and lower segments.Once an iris region is detected as occluded by the upper eyelid, the proposed algorithm detects the darkest pixel of each column in the upper part of the iris region as the eyelid boundary point in that column.The pixels above the eyelid boundary are removed for each column in the union regions of Sa and Su.Lower eyelid removal that occludes the iris is similar to the upper eyelid removal.Figure 5 shows the result of each step in the proposed eyelids localization algorithm for an eye-image occluded by eyelids.

Figure 1 :
Figure 1: The flow chart of the proposed scheme.

Figure 2 :
Figure 2: The flow chart of the proposed iris detection algorithm.

Figure 3 : 6 Mathematical
Figure 3: The result of the proposed inner and outer boundaries localization algorithm: (a) original RGB color iris image, (b) corresponding -component image, (c) candidate pupil region after outliers detection and morphological opening operation, (d) detected inner iris-border, (e) pupil-removed eye-image in grayscale, (f) candidate iris region after redundant areas removal, (g) edge map of (f) created by Sobel detector, (h) outer iris-border by OMT from the edge map, (i) outer iris-border after DKCF three times, (j) the best fit circle for final inliers (circle in red) and the best fit circle for points detected by OMT (circle in blue), (k) inner and outer iris-boundaries located by the proposed scheme, and (l) the detected iris region.

Figure 4 :
Figure 4: Segmented regions and columns' pixel value distributions with respect to -coordinate.

Figure 6 :
Figure 6: The result of the proposed eyelash segmentation algorithm: (a) iris image with eyelids removal, (b) grayscale image of (a), (c) the histogram of the region just below upper eyelid, (d) the histogram of the region just above lower eyelid, (e) the histogram-plot of the below upper eyelid and the above lower eyelid regions, where the black vertical dash line indicates the intensity threshold for eyelashes detection, and (f) the result of the proposed eyelash segmentation algorithm.

Table 4 : 5 :
Samples result of the proposed algorithm for eyelash occlusion eye-images from the UBIRIS.1 database.Performance comparison between Vatsa et al. 's[19] and the proposed methods for heavy noise eye images.

Table 1 :
Performances of the proposed algorithm for eyelash occlusion eye-images.

Table 2 :
MOEs of the proposed algorithm for the UBIRIS.v1.

Table 3 :
The accuracy of relative methods for the 1214 eye-images of the UBIRIS.v1.