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This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Iris segmentation in the iris recognition systems is a challenging task under noncooperative environments. In this paper, we propose a pupil localization method for locating the pupils in the non-close-up and frontal-view iris images that are captured under near-infrared NIR illuminations and contain the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and occlusions by the eyelids, eyelashes, and eyebrow hair.
In the proposed method, first, a novel edge-map is created from the iris image, which is based on combining the conventional thresholding and edge detection based segmentation techniques, and then, the general circular Hough transform CHT is used to find the pupil circle parameters in the edge-map.
Our main contribution in this research is a novel edge-map creation technique, which reduces the false edges drastically in the edge-map of the iris image and makes the pupil localization in the noisy NIR images more accurate, fast, robust, and simple. The proposed method was tested with three iris databases: The average accuracy of the proposed method is Iris recognition [ 1 — 3 ] is one of the most accurate and secured methods of identifying persons among all the available biometric identification techniques.
An iris recognition algorithm typically consists of three stages: The iris segmentation is the first module in an iris recognition system, whose input binary edge map matlab an iris image Figure 1 captured by the image acquisition system [ 2 ].
The iris images captured under near-infrared NIR illuminations are preferred over visible binary edge map matlab VW light because the irises reveal rich and binary edge map matlab features in the NIR wavelengths [ 34 ]. Therefore, most of the available iris databases are the NIR images [ 56 ]. The quality of the iris images decides the complexity of the iris segmentation algorithms [ 5 ]. The iris images from the NIR databases can be categorized into two types: The unconstrained or noisy iris images Binary edge map matlab 1 b may have different types of the noise, such as specular and lighting reflection spots, eyeglasses, nonuniform illumination, low contrast, and obstructions by eyelids, eyelashes, and eyebrows [ 7 ].
One type of nonideal binary edge map matlab that iris images may have is the nonfrontal view, when the user is not looking ahead towards the camera. In this paper, we propose a novel edge-map creation technique for the noisy NIR images that reduces the false edges drastically so that the pupil can be localized accurately and rapidly using a general circular Hough transform CHT algorithm. We have also implemented the CHT to find the radius and center of the pupil circle.
However, the novelty of our algorithm lies binary edge map matlab the edge-map creation technique that acts as input for the CHT. The proposed method targets the non-close-up and frontal-view NIR images that are captured in the unconstrained environments and have the noise issues as discussed previously Figure 1 b.
To examine the binary edge map matlab, the proposed method was tested with three different iris databases: The sample iris images from these databases are shown in Figure 4. In this paper, we considered the pupil localization, which is an important step in the iris segmentation for two reasons: The remainder of binary edge map matlab paper is organized as follows: Section 2 discusses related work and Section 3 explains the proposed pupil localization method and its implementation.
Section 4 presents the experimental results and discussion, whereas Section 5 concludes the work. Daugman proposed an integrodifferential operator IDO that acts as a circular edge detector to localize the iris boundaries, whereas Wildes method uses the Hough transform HT to detect the circular edges.
However, these iris localization methods work under the controlled binary edge map matlab and their performance strongly deteriorates when dealing with the noisy data [ 7 ]. Some of the state-of-the-art iris segmentation methods along with the main techniques they use are provided in the tabular form in [ 10 ], where most of the methods are based on the Hough transform HT technique.
Some of binary edge map matlab recent methods to localize the irises in the noisy NIR and VW images are described in [ binary edge map matlab ], [ 8 ], [ 11 — 13 ], and [ 14 — 16 ], respectively. The HT based techniques consider the iris as a circular ring and the CHT is used to detect the circles as discussed in [ 81416 ], but the iris contours may not be the perfect circles in the non-frontal-view iris images.
The iris localization methods for the NIR images, available in the literature, localize the pupil using either the intensity thresholding based segmentation [ 1217 ] or the edge detection based segmentation [ 81819 ] techniques. In the edge detection and HT based methods, first an optimized edge-map of the iris image is created to reduce the false edges in the edge-map, so that the pupil boundary can be localized accurately using the CHT as described in [ 819 ].
The creation of the optimized edge-map of the iris image becomes more challenging if the images are noisy such as the CITHV4 database images. The binary edge map matlab iris images are first preprocessed for removing the reflections, adjusting the nonuniform illuminations, and enhancing the contrast [ 7binary edge map matlab11 ].
In addition, the edge-map created from the preprocessed image is further optimized to reduce the false edges. One complex approach to create the optimal edge-map for the pupil localization in the noisy NIR images is described in [ 8 ]. The researchers have used either the techniques to get the optimal edge-map of the iris image [ 819 ] or the modified CHT algorithms [ 20 — 22 ] for localizing the pupils in the iris images. The complexity of algorithms increases in order to achieve high accuracy binary edge map matlab noisy images, which in turn increases the computation time [ 1113 ].
The proposed method achieves the pupil localization in the noisy NIR images in two phases: The objective of Phase 1 is to prepare appropriate input for Phase 2, binary edge map matlab that the pupil circle can be detected accurately and rapidly. In Phase 1, the edge-map of the iris images is created using a combination of different image segmentation techniques, which are thresholding, morphological processing, and edge detection.
In Phase 2, a general CHT algorithm is implemented to detect a circle in the edge-map by specifying a range of radii as input. The proposed method is described in Figure 2. Phase 1 and Phase 2 of the proposed method are discussed below. The binary edge map matlab, eyelashes, binary edge map matlab, eyebrow hair, black eyeglass frames, and nonuniform low illumination regions are darker regions in the iris image.
The binary edge map matlab of these dark regions other than the pupil complicates the pupil detection process if thresholding based region segmentation technique is used [ 7 ]. If the edge detection based segmentation technique is used for detecting the pupil in the iris image, many false edges appear in the edge-detected image of the iris image due to the reflection spots, eyelids, eyelashes, eyebrows, and eyeglass frames.
So, neither of the segmentation techniques is able to provide an binary edge map matlab edge-map input to the CHT algorithm. To create an optimal edge-map of the iris image, we use a technique in which the intersection operation logical ANDing is applied on the two edge-detected images obtained via the above-mentioned segmentation techniques as illustrated using Figure 5. The proposed edge-map generation technique uses the following image processing steps to get the final optimal edge-map for the pupil localization.
The eye image is smoothed using a Gaussian filter [ 2324 ] of size and sigma equal to 1. The larger filter size makes the binary edge map matlab more blurred and reduces the pupil binary edge map matlab contrast.
The sigma equal to 1. The smoothing of the iris image removes the random noise and the uneven intensities that may result in unnecessary false edges in the edge-detected iris image.
It also helps in the image binarization step by reducing the false black pixels in the binary image as described later. The smoothed iris image is shown in Figure 2 b. The Sobel edge detector without thinning operation [ 2324 ] is applied on the smoothed iris image.
Two Sobel filter masks are used to find -derivative and -derivative components of image gradient as described in [ 23 ].
A suitable threshold value is chosen in binary edge map matlab edge detection so that the pupil edges, which are among the strong edges in the image, are detected and the faint edges are not. The higher threshold gives fewer edge pixels and lower threshold gives more edge pixels. The final value of threshold is chosen after a number of iterations of simulation and manual inspection of edge-detected images, and then it remains constant for a given database.
Figure 2 f shows the edge-detected image after the Sobel operator is applied on the smoothed iris image Figure 2 b. The edge-detected image has the pupil edges and other false edges due to the lighting reflection spots, eyelids, eyelashes, and eyeglass frames as shown in Figure 2 f. If the CHT is applied binary edge map matlab this edge-detected image to detect the pupil circle, it may take much computation time due to the false edges and the accuracy binary edge map matlab the circle detection would be low.
The pupil is the dark region in the iris image. The intensity-based thresholding [ 23 ] is used to segment the pupil region. The global thresholding with threshold value binary edge map matlab applied on the Gaussian smoothed iris image to get the binarized-image binary image as shown in Figure 2 c.
The binary image,is obtained from intensity image,using. The initial value of is determined by finding average intensity of pixels in pupil region excluding reflections inside the pupil, for a few images in a given database, and then it was adjusted by performing simulations on all the database images using MATLAB and visualizing the binary images obtained after image binarization.
The final value of is chosen after binary edge map matlab number of iterations of simulation and manual inspection of output images. This value remains the same for all the images of a database and do not need to be computed for each binary edge map matlab image.
The values are different for different databases. The objective of this step binary edge map matlab to reduce the size of the noise due to eyelids, eyelashes, and eyebrow hair in the binary image of Figure 2 c. This is achieved by applying the morphological processing on the binary image [ 24 ] as follows. The image opening operation for black objects is applied on the binary image using a structuring element of type disc of size.
Before the image opening, a hole filling operation [ 25 ] is applied on the complemented image of the binary image to fill white dots in the pupil. The white dots in the pupil are due to the reflections caused by the light source while capturing the image. Figure 2 d shows the cleaned binary image in which the noise due to eyelids and eyelashes is completely removed. If the noise is not removed completely after the image opening, its effective size reduces, because the binary edge map matlab regions i.
The Sobel edge detection without thinning is applied on the cleaned binary image, Figure 2 dobtained because of the previous step. The resulting edge-detected image may have the false edges corresponding to the dark illumination and eyeglass frame as shown in Figure 2 e. The other false edges in Figure 2 e could be due to the noise of eyelids, eyelashes, and eyebrow hair, if this noise binary edge map matlab not completely removed in the previous step binary image cleanup.
The characteristic of the dark illumination is that binary edge map matlab is not detected when the edge detection is applied on the intensity iris image as shown in Figure 2 f.
Therefore, the intersection operation on the two edge-detected images, Figures 2 e and binary edge map matlab fremoves the false edges due to the dark illumination as shown in Figure 2 g. Figure 2 g has a few false edges due to the eyeglass frame only. The edge-map in Figure 2 g is called an optimal edge-map of the iris image as it has significantly less false edges as compared to Figure 2 e or Figure 2 f. The intersection operation also removes completely or partially the false edges due to the noise of eyelids and eyelashes, if these false edges are present in the edge-detected image of the cleaned binary image see Figure 3.
This so happens because the noise-size of eyelids and eyelashes in the cleaned binary image is smaller than the noise-size that is detected by the edge detection on the intensity iris image. Figure 3 shows that the noise of eyelids and eyelashes is present in the cleaned binary image Imagebut the false edges binary edge map matlab to this noise are almost completely removed in the optimal edge-map Image due to the intersection operation.
Figure 3 a shows an ideal case, where the false edges are completely removed and the optimal edge-map Image contains the pupil contour only, whereas Figure 3 b shows the case, where the optimal edge-map Image contains a few false edges also. In Figure 3 bthe pupil in the original iris image Image binary edge map matlab occluded by the binary edge map matlab and eyelashes due to which the complete pupil contour does not appear in the optimal edge-map Imagebut still the pupil is localized accurately by the CHT algorithm.
The CHT algorithm is discussed binary edge map matlab. We implemented the CHT algorithm [ 26 ] to find the pupil circle radius and the pupil position in the edge-map of the iris image. The input to the CHT algorithm is a range of radii, that is, minimum and maximum radius of the pupil circle. At every edge-pointwhich is a white pixel in the edge-map, the virtual circles are drawn with different radii using.
A 2-dimensional 2D accumulator array of size the same as the image is created and initialized to zero. The accumulator size can also be smaller than the image size, if we can somehow estimate the position of the pupil in the image.