Iris recognition is a special type of biometric system, which is used to identify a person by analyzing the patterns in the iris. It is used to recognize the human identity through the textural characteristics of one's iris muscular patterns. Although eye colour is dependent on heredity, iris is independent even in the twins. Out of various biometrics such as finger and hand geometry, face, ear and voice recognition, iris recognition has been proved to be one of the most accurate and reliable biometric modalities because of its high recognition. Iris recognition involves 5 major steps. Firstly, image acquisition is done in which the image is captured by a high resolution camera, then the iris and the pupillary boundary are extracted out from the whole eye image, which is called segmentation. After segmentation, the circular dimension is converted to a fixed rectangular dimension which is called normalization. From this normalised image, the feature is extracted from Gabor filter, DFT, FFT, etc. At last, the iris code is matched using Hamming distance and Euclidean method. This project focuses on iris segmentation. Iris segmentation is the most important part in the iris recognition process because the areas that are wrongly considered as the iris regions would corrupt the biometric templates resulting in a very poor recognition [16-21] The main objective of iris segmentation is to separate the iris region from the pupil and sclera boundaries. There are various methods for segmenting the iris from an eye image to give a best segmented result. In this project, iris segmentation is done using Daugman's integro differential method and Circular Hough Transform to find out the pupil and the iris boundaries. Iris images are taken from the CASIA V4 database, and the iris segmentation is done using Matlab software where iris and pupilary boundaries are segmented out. The experimental result shows that 84% accuracy is obtained by segmenting the iris by Circular Hough Transform and 76% accuracy is obtained by segmenting the iris through Daughman's method. It is concluded that, the Circular Hough Transform method of iris recognition is more accurate than the Daughman's method.
The term “biometrics” is derived from the Greek words “bio” meaning life and “metric” meaning to measure. Biometrics is defined as the measurement and statistical analysis of the people's physical and behavioural characteristics. The basic principle of biometric authentication is that, every person is unique and every individual can be differentiated by his or her intrinsic physical or behavioural traits. Iris is made up of many layers which gives an unique appearance. Under high resolution camera, and with illumination, the uniqueness of the iris is visually plain when we look at its each and every detail under.
The iris recognition system is the system of recognising a person by analysing the random pattern of iris. Iris recognition can be considered as one of the most reliable and accurate method of biometric technology when compared with other biometric technologies such as face, fingerprint, and speech recognition [17] - [9]. Segmentation is to subdivide an image into its component regions or objects. It should stop when the objects of interest in an application have been isolated. It is a process of localizing iris from the entire part which are acquired. Iris can be approximated by two circles.
The iris recognition system (Figure 1) has five major components, viz.,
1. Image Acquisition
2. Image Segmentation
3. Image Normalisation [22]
4. Image Extraction
5. Iris Template Matching
Figure 1.Flow chart of Iris Recognition
Segmentation is the process of accurately separating the iris from the whole eye image. In segmentation, the eye information is focused on iris portion and all the other parts of the eye like sclera, eyelashes, eyelid, etc, are ignored. The data acquired from the iris portion are very useful to identify and authenticate each user, because the iris portion is divided into multiple segments. The eyelids and eyelashes hide the upper and lower portion of the iris region[2], [3]. The accuracy of segmentation is totally dependent on the imaging quality of an eye image. Data having noise and error representation causes poor recognition rates [13], [25], [2].
The proposed methodology uses Daughman's Integro Differential Operator (DIO) algorithm. Daughman makes use of an integro-differential operator for segmenting the circular iris and pupil regions and also the arcs of the upper and lower eyelids. Figure 2 shows the flowchart of Daughman’s method.
Daugman's formula is given below.
where I(x, y) is the eye image,
r is the radius, and
G(r) is the Gaussian smoothing function and contour of the iris.
The smoothed image is scanned for a circle that has a maximum gradient change, which indicates an edge.
The operator searches for the circular path by the change in the maximum pixel values by varying the radius and the centre of x and y positions of the circular contour to attain the precise location of the eyelids [10]-[12].
The DIO also uses the first derivatives of the image to find geometric parameters. Thresholding problem is avoided like in Hough transform DIO that uses basic raw derivative information [14], [15], [26].
Figure 2. Flowchart of Daughman’s Algorithm
The Hough transform is a standard algorithm that is used to determine the parameters of geometric objects, such as lines and circles which are present in an image [23], [24]. It is the transformation of a point in the x, y-plane to the parameter space. Figure 3 shows the CHT method.
The circle is simple to represent in the parameter space, as compared to other shapes, because the parameters of the circle can be directly transferred to the parameter space.
Figure 3. Flowchart of Circular Hough Transform
The equation of a circle is
Here, r is the radius;
a & b are the center of the circle in the x and y directions respectively.
The parametric representation of the circle is,
The Circular Hough Transform can be used to find out radius and centre coordinates of the pupil and the iris regions [30].
The input image is taken from the CASIA (Chinese Academy of Sciences-Institute of Automation) database, which contains 756 grayscale eye images with 108 unique eyes and 7 different images for each unique eye. Images from each class are taken from two sessions within a month interval between the sessions. The eye images are mainly from Asian decants, whose eyes are characterized by irises that are densely pigmented, with dark eyelashes. Features in the iris region are highly visible due to the specialized imaging conditions using the infrared light, and there is good contrast between the pupil, iris and sclera the regions [4] .
The input image is cropped to a dimension [150*150]. By cropping the image, we can remove the eyelids, eyelashes and the sclera, which causes errors in the iris segmentation.
The authors have cropped the image in a rectangular dimension rather than a circular dimension, because the iris which is in an oval shape, does not support a circular dimension [250*150].
The image is converted from RGB (Red, Green, Blue) to gray because of which we can easily detect the changes in pixel value that occurs between the pupil and the iris.
The radius and the centre coordinates are initialized as the radius and the coordinates can be placed within the particular range of radius .
Here, thresholding is applied for successful implementation of the Daughman method. Before thresholding, the pixels are called as object pixels, which are darker than the background pixels and are called as centre pixels. The centre pixels for both the iris and the pupil lie inside the dark pupil region. A range of [0,1] for intensity values of the pixels in the eye images has been selected, where 0 represent the black pixels and 1 represent the white pixels. To get the best result, all the object pixel values below 0.5 are marked before being implemented. Here , the Rmax value should not be more than100, or else an error occurs.
The iris is segmented out by applying all the methods. Some iris images are not properly segmented out. The iris is segmented by applying an accurate radius to get the segmented output of the iris.
The input iris image is taken from the CASIA (Chinese Academy of Sciences-Institute of Automation) database. It contains 756 grayscale eye images with 108 unique eyes and 7 different images of each unique eye. The eye images are from Asian decants, whose eyes are characterized by irises that are densely pigmented, and with dark eyelashes. In CASIA images, the iris features are highly visible because of the specialized imaging conditions using infrared light and between the pupil, iris and the sclera regions, there is a good contrast.
Input image is cropped to dimension [150*150]. By cropping the image, we can remove the eyelids, eyelashes and the sclera, which causes errors in the iris segmentation. The image is cropped in rectangular dimension rather than circular dimension because an iris is an oval shape, which does not support circular dimension [250*150].
The authors convert the image from RGB (Red, Green, Blue) to gray because of which, we can easily detect the changes in pixel value that occurs between the pupil and the iris.
The edges are detected using Canny Edge detector. Canny Edge detector is considered to be the best edge detector for finding out the edges in an image.
Circular Hough transform is applied after edge detection to find out the circle in an image.
The authors use the neighbourhood suppression method of peak finding to ensure that we find the spatially separated circles.
The circles found in the image, are drawn using both the positions and the radii stored in the peaks array. The circle point's function is convenient for this.
This is the last process where we get the segmented image of the iris boundaries.
In this project, the following results are found using the Daughman’s method and the Circular Hough Transform method for iris.
Table 1 shows the accurately segmented iris using the Circular Hough Method. Table 2 shows when the minimum radius is changed as, Rmin >42. The accurately segmented iris using Daughman’s method is shown in Table 3. Error in the image occurs when Rmin is constant and Rmax is more than 100 during segmentation as shown in Table 4. When the maximum radius, Rmin value is changed, their results are shown in Table 5.
Table 1. Accurately Segmented Iris using the Circular Hough Method
Table 2. Minimum Radius is Changed in Circular Hough Method
Table 3. Accurately Segmented Iris using Daughman’s Method
Table 4. Error in the Image
Table 5. Show when maximum radius Rmin value is changed
From the results shown above, it is found out that the eye input image is segmented out using Circular Hough Transform and Daughman’s intro differential operator. Images are taken from the CASIA database. Hit and trial method is used to find out the segmented output of iris and pupilary boundary.
In this project, 100 CASIA images are taken. These images are segmented using two different methods in which, 50 where of Daughman method and 50 of Circular Hough Transform. The first method used is Circular Hough Transform, where the accurately segmented image is 42 out of 50 eye images.
The second method is Daughman’s method, in which 38 images are accurately segmented out of the 50 eye images. It is observed that in Daughman’s method, if the Rmax value is greater than 100, then the image will not get max segmented and error occurs. Similarly, if the value of Rmin is less than 0, then also the error occurs and the image will not be segmented out and the threshold value should be less than 5 to obtain a better result. Such errors were not found in Circular Hough Transform, as when we put the Rmax value greater than 100, there will be no error and the image gets segmented out [27]-[29], [31].
Table 6 shows that, Circular Hough Transform has more percentage of accuracy than the Daughman’s method. The Circular Hough Transform accuracy percentage is 84%, whereas the Daughman’s method has the accuracy percentage of 76%.
Table 6. Accuracy Percentage of Segmentation
Hence, the total result of Iris segmentation shows that Circular Hough Transform method has better segmentation than the Daughman's method.
Iris recognition system is the system of recognising a person by analysing the random patterns of iris. Iris recognition can be considered as one of the most reliable and accurate methods of biometric technology, when compared with other biometric technologies such as face, fingerprint and speech recognition of segmenting the iris from the whole eye image. In iris recognition, there are 5 major steps namely, image acquisition, iris segmentation, iris normalisation, iris feature extraction, and iris template matching. In this paper, research has been done on the iris segmentation. Iris segmentation is the process of segmenting the iris portion from the whole eye image. It is the most important task in iris-based biometric authentication system and if the iris part of an eye image is not detected precisely, then an error is created in the overall iris recognition method. The work has been stressed more on efficient and accurate iris segmentation method using Circular Hough Transform and Daughman’s method for developing the best biometric identity. The Circular Hough Transform and Daughman’s method segment the iris portion by using hit and trial method and segments out the iris and the pupilary boundary. The percentage of accuracy obtained by both the methods are different.
In this paper, 100 CASIA images were taken, in which 50 images were segmented from Circular Hough Transform and another 50 from Daughman’s method. It is found that, the percentage of segmentation in Circular Hough Transform is more as compared to the Daughman’s method. The accuracy percentage of Circular Hough Transform is 84%, whereas the accuracy percentage of Daughman’s method is 76%. The eye image is taken from the CASIA V1 database and MATLAB software are used to perform the segmentation. Therefore, it is concluded that iris segmentation using Circular Hough transform provides a much better segmentation accuracy than the Daughman's Method.
From this research work, extensive future scope can be carried out.
