An Efficient Iris Segmentation Technique based on a Multiscale Approach

The use of biometric signatures, instead of tokens such as identification cards or computer passwords, continues to gain increasing attention as an efficient means of identification and verification of individuals for controlling access to secured areas, materials, or systems and a wide variety of biometrics has been considered over the years in support of these challenges. Iris recognition is especially attractive due to the stability of the iris texture patterns with age and health conditions. Iris image segmentation and localisation is a key step in iris recognition and plays an essential role the accuracy of matching. In this paper, we propose a new iris segmentation technique using a multiscale approach for edge detection, which is a fundamental issue in image analysis. Due to the presence of speckles, which can be modelled as a a strong multiplicative noise, edge detection for iris segmentation is very important and methods developed so far are generally applied in one single scale. In our proposed method, we introduce the concept of multiscale edge detection to improve iris segmentation. The technique is effecient for edge detetcion, greatly reduces the search space for the Hough transform and at the same time is robust to noise thus improving the overall performance. Linear Hough transform has been used for eyelids isolation, and an adaptive thresholding has been used for isolating eyelashes. Once the iris is segmented, a normalization step has been carried out by converting an iris image from cartesien into polar coordinates which are more suitable to deal with rotation and translation problems. Extensive experiments have been carried out and results obtained have shown an effectiveness of the proposed method which provides a high segmentation success of 99.6%

An Efficient Iris Segmentation Technique based on a Multiscale Approach

The use of biometric signatures, instead of tokens such as identification cards or computer passwords, continues to gain increasing attention as an efficient means of identification and verification of individuals for controlling access to secured areas, materials, or systems and a wide variety of biometrics has been considered over the years in support of these challenges. Iris recognition is especially attractive due to the stability of the iris texture patterns with age and health conditions. Iris image segmentation and localisation is a key step in iris recognition and plays an essential role the accuracy of matching. In this paper, we propose a new iris segmentation technique using a multiscale approach for edge detection, which is a fundamental issue in image analysis. Due to the presence of speckles, which can be modelled as a a strong multiplicative noise, edge detection for iris segmentation is very important and methods developed so far are generally applied in one single scale. In our proposed method, we introduce the concept of multiscale edge detection to improve iris segmentation. The technique is effecient for edge detetcion, greatly reduces the search space for the Hough transform and at the same time is robust to noise thus improving the overall performance. Linear Hough transform has been used for eyelids isolation, and an adaptive thresholding has been used for isolating eyelashes. Once the iris is segmented, a normalization step has been carried out by converting an iris image from cartesien into polar coordinates which are more suitable to deal with rotation and translation problems. Extensive experiments have been carried out and results obtained have shown an effectiveness of the proposed method which provides a high segmentation success of 99.6%

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets

Iris Data Indexing Method Using Biometric Features 1

Abstract A biometric system provides identification of an individual based on a unique feature or characteristic possessed by the individual. Among the available biometric identification system, Iris recognition is regarded as the most reliable and accurate one. Demands are increasing to deal with large scale databases in these applications. The Segmentation in boundary detection, edge Mapping, circular Hough Transform, extracting Region of interest (Eyelash and noise removal), circle detection. In a module of Person Identification system using Iris Recognition. The iris recognition system consists of a segmentation that is based on the Hough transform and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes and reflections. The extracted iris region was normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the data from Gabor filters was extracted and quantized to encode the unique pattern of the iris into a biometric template. To improve the efficiency of computational method and accuracy of classification, the Difference metric and subtraction method was employed. It was observed that this method classify the images with better accuracy. The Hamming distance was employed for classification of iris templates. The iris recognition is shown to be a reliable and accurate biometric technology. Keywords Gabor Filter Process, Image Recovery, Iris Biometric, Personal Verification I. Introduction The advances in Information technology and the increasing requirement of security issues have resulted in a rapid development of person identification based on biometrics. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina, and the one concentrated and presented in this paper, the iris. Iris is regarded as the reliable and accurate technique because iris forms during gestation period itself and remains the same for the rest of one's life and it is unique for individuals. Iris is well protected and extremely difficult to modify. Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital color image for face recognition, or taking a digital color image for iris recognition. The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. A good biometric is characterized by use of a feature that is; highly unique -so that the chance of any two people having the same characteristic will be minimal, stable -so that the feature does not change over time, and be easily captured -in order to provide convenience to the user, and prevent misrepresentation of the feature