IRIS Segmentation Using Geodesic Active Contour Method (original) (raw)

IRIS Recognition System Using Geodesic Active Contours for Non-Ideal IRIS Images

2015

The stable unique epigenetic pattern of the iris make it a robust biometric trait for personal identification. The first stage of iris recognition is to isolate the actual iris region in a digital eye image. The segmentation stage is critical to the success of an iris recognition system, since data that is falsely represented as iris pattern data will corrupt the biometric templates generated, resulting in poor recognition rates. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under non ideal conditions using such conic models. In this paper we use Geodesic Active Contours (GAC) for segmenting iris from the surrounding structures. Since active contours can 1) assumes any shape and 2) segment multiple objects simultaneously. Experimental Results on the UBIRIS ...

Iris Segmentation using Geodesic Active Contour for Improved Texture Extraction in Recognition

International Journal of Computer Applications, 2012

Automatic identification/verification of a person through biometrics has been getting extensive attention due to an increasing importance of security. The most popular biometric authentication scheme employed for the last few years is Iris Recognition. The performance of iris recognition system highly depends on segmentation. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented accurately. The iris proposed recognition module consists of the preprocessing system, segmentation, feature extraction and recognition. Mainly it focuses on image segmentation using Geodesic Active Contours and comparison with traditional methods of segmentation. As active contours can 1) assume any shape and 2) segment multiple objects at the same time, they lessen some of the concerns related with conventional iris segmentation models. The iris texture is extracted in an iterative fashion by considering both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA (Institute of Automation, Chinese Academy of Sciences) Interval version3 iris databases implemented in MATLAB shows the efficiency of the proposed technique application.

Iris segmentation using geodesic active contours

2009

Abstract The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses.

IRJET-Application of Geodesic Active Contours in iris Segmentation

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is the most reliable and accurate biometric identification system. The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris. Most iris recognition systems consist of an automatic segmentation system that is based on the Hough transform. These systems localize the circular iris and pupil region. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures is described. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image.

Segmentation of Iris from Human Eye Image using Active Contour Model

Biometric system provides automatic identification of an individual based on a unique features or characteristic possessed by the individual. Iris recognition is one of the important biometric recognition systems that identify people based on their eyes and iris. In this paper, we proposed a method to segment the iris in the eye image using active contour method. We evaluated our proposed method using the image obtained from the internet.

Segmenting non-ideal irises using geodesic active contours

2006

Abstract The richness and the apparent stability of the iris texture makes it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to isolate the iris structure from the other components in its vicinity, viz., the sclera, pupil, eyelids and eyelashes. Most segmentation models in the literature assume that the pupillary, the limbic and the eyelid boundaries are circular or elliptical in shape.

Iris Segmentation using Geodesic Active Contours and GrabCut

Iris segmentation is an important step in iris recognition as inaccurate segmentation often leads to faulty recognition. We propose an unsupervised, intensity based iris segmentation algorithm in this paper. The algorithm is fully automatic and can work for varied levels of occlusion, illumination and different shapes of the iris. A near central point inside the pupil is first detected using intensity based profiling of the eye image. Using that point as the center, we estimate the outer contour of the iris and the contour of the pupil using geodesic active contours, an iterative energy minimization algorithm based on the gradient of intensities. The iris region is then segmented out using both these estimations by applying an automatic version of GrabCut, an energy minimization algorithm from the graph cut family, representing the image as a Markov random field. The final result is refined using an ellipse-fitting algorithm based on the geometry of the GrabCut segmentation. To test our method, experiments were performed on 600 near infra-red eye images from the GFI database. The following features of the iris image are estimated: center and radius of the pupil and the iris. In order to evaluate the performance, we compare the features obtained by our method and the segmentation modules of three popular iris recognition systems with manual segmentation (ground truth). The results show that the proposed method performs as good as, in many cases better, when compared with these systems.

AUTOMATIC SEGMENTATION OF HUMAN IRIS IMAGES USING LOCALIZED ENERGY- BASED ACTIVE CONTOUR MODEL

Biometric identification is one of the technologies used for automated personal identification. Iris recognition is biometric-based identification method which are more reliable and accurate authentication system. This paper present a simple and efficient method based on localized energy-based active contour model to segment iris from human eye image. First we segment the rough iris image and it is then redefined in the second level segmentation to produce better result. This proposed method is tested with the eye images obtained from CASIA database. The performance of the method is quantitatively evaluated by calculating the similarity measures Jaccard (J) and Dice (D). Experimental results show that the proposed method provides better segmentation result compared to the expert segmented images. .

Iris Recognition Using Active Contours

— The division is the urgent stage in iris acknowledgment. We have utilized the worldwide limit an incentive for division. In the above calculation we have not considered the eyelid and eyelashes relics, which corrupt the execution of iris acknowledgment framework. The framework gives sufficient execution likewise the outcomes are attractive. Assist advancement of this technique is under way and the outcomes will be accounted for sooner rather than later. Based on the reasonable peculiarity of the iris designs we can anticipate that iris acknowledgment framework will turn into the main innovation in personality verification.In this paper, iris acknowledgment calculation is depicted. As innovation advances and data and scholarly properties are needed by numerous unapproved work force. Therefore numerous associations have being scanning routes for more secure confirmation strategies for the client get to. The framework steps are catching iris designs; deciding the area of iris limits; changing over the iris limit to the binarized picture; The framework has been actualized and tried utilizing dataset of number of tests of iris information with various complexity quality.

ACTIVE CONTOUR BASED BI-LEVEL SEGMENTATION OF IRIS FROM THE HUMAN EYE IMAGES

Iris recognition is one of the biometric authentication system available today to authenticate the human being. It is more accurate and reliable identification system. Because iris is unique to each individual, and even among the identical twins or between the left and right eye of the same person. Iris recognition refer to the identification of iris based on some computational algorithm. In this paper, we proposed a method to segment the iris in the eye image using localized energy-based active contour method. We evaluated our proposed method using the image obtained from the internet. Our proposed method works better even for the images having high degree of noise and low contrast.