Iris Recognition and Automated Eye Tracking

Physiological and behavioral characteristics of individuals that distinguish one person from the others. These characteristics are different in each person Iris is the best characteristic that can be used for person’s identification and authentication in comparison with fingerprints, face, voice, and signature. The iris pattern is different between the right and left eye of the same person. For this recognition system we have used MATLAB tool. For iris recognition first of all database will be created in MATLAB using webcam. Then iris localization is done, for iris identification. After localization normalization and segmentation will performed, for that hough transform algorithm implemented. In last binarization of image is performed. Then binary output is compared with database image’s binary value. From comparison recognition is done, we can identify whether person is authorized or not

FRAMEWORK JARINGAN SYARAF TIRUAN DENGAN ALGORITMA GENETIKA PADA PENGENALAN IRIS MATA

Iris recognition is a secure and reliable biometric identification system for user detection. Used to take a portrait of a person. This system was created by combining the artificial neural learning method with genetic algorithms. Implementation of this recognition system through several processes, namely the collection of iris data, iris data obtained through the acquisition process with image output. The recognition system was built using Matlab software, and the obtained images were separated into two parts: training images and test images. The training image is pre-processed. The iris recognition system's performance is evaluated using segmentation. Segmentation is used to locate the right iris region in a certain section of the eye and must be done exactly and accurately to eliminate the iris area's  eyelashes, eyelids, reflections, and pupillary noise. We use the Daughman Algorithm segmentation of Iris Recognition in this study. In this research, we apply Daughman Algorithm segmentation for Iris Recognition. To reduce dimensional differences across the iris area, the segmented iris regions were normalized. The convolution theorem is used to code the characteristics of the iris. As a match metric, Hamming distance is included, which offers a count of how many mismatched bits there are between the iris templates. Pre-processing aids the identification, which includes training and testing. The pre-processing findings are used as input data in the training phase, whereas test image data is used in the testing phase. The use of artificial neural learning as well as a genetic algorithm to detect the iris pattern is effective and achieves the objectives. This is corroborated by the 95% recognition accuracy rate. According to the test findings, the clarity of the produced iris picture, the number of hidden mark sheet, the quantity of epoch parameters, as well as the appearance of the training sample are the criteria that determine the system's recognition rate

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

The need for reliable user recognition (identification/authentication) techniques has grown in response to heightened security concerns and accelerated advances in networking, communication, and mobility. Biometrics, defined as the science of recognizing an individual based on his or her physical or behavioral characteristics, is gaining recognition as a method for determining an individual\u27s identity. Various commercial, civilian, and forensic applications now use biometric systems to establish identity. The purpose of this paper is to design an efficient multimodal biometric system based on iris and retinal features to assure accurate human recognition and improve the accuracy of recognition using deep learning techniques. Deep learning models were tested using retinographies and iris images acquired from the MESSIDOR and CASIA-IrisV1 databases for the same person. The Iris region was segmented from the image using the custom Mask R-CNN method, and the unique blood vessels were segmented from retinal images of the same person using principal curvature. Then, in order to aid precise recognition, they optimally extract significant information from the segmented images of the iris and retina. The suggested model attained 98% accuracy, 98.1% recall, and 98.1% precision. It has been discovered that using a custom Mask R-CNN approach on Iris-Retina images improves efficiency and accuracy in person recognition

AN EFFICIENT APPROACH FOR TEXTURED CONTACT LENSES DETECTION IN IRIS RECOGNITION

The effective implementation associated with a product is largely based on its reliability,authenticity and the quantity of secrecy it offers.In the current high techno world where privacy and security are the worries of prime importance,the important systems must employ techniques to do this. Our project is simply a small step towards this.The iris based system can deal with  lots of individual biological versions but still supply the identification system with a lot more precision and reliability. Within this project we've developed a system that involves recognition of the person using IRIS like a biometric parameter.We've first segmented the pupil and iris structure in the original eye image.Whenever we have normalised it to construct an element vector which characterizes each iris clearly.This selection vector will be employed for matching among various templates and identifies the person. The job provided within this project is principally targeted at supplying a recognition system to be able to verify the distinctiveness of human iris as well as its performance like a biometric.The paper has implementation of calculations on CASIA database.The different outcomes of different implementations as well as their accuracies happen to be examined within this paper. Overall within this paper,a truthful effort in recommending a competent system for implementation from the human identification system according to iris recognition and powerful recognition of textured contact contacts in iris recognition of images. This project presents an Iris recognition system, that was examined on CASIA iris image database, to be able to verify the stated performance of iris recognition technology

Identification of Identical Twins using Face Recognition with Results

Face recognition is a process used to identify or verify the person based on digital image from unique face of humans. Face recognition is based on individual and unique person identification. This process fully based on comparing the image with other person image for identification. Face Recognition is typically used in security systems and can be compared with other biometrics such as fingerprint or iris recognition systems. Here, the major problem is to identify twins. To overcome this problem we can use different facial recognition algorithms. The facial recognition algorithms should be able to identify the similar-looking individuals or identical Twins with accurate classification. In the proposed system, image of a person is given as a input then different features of image were extracted by using the Gabor and LBP algorithms. Extracted Features of both the images are compared and then classified using multi-SVM classifier. Based on classification method, the persons were identified to be identical twins or they were identified to be same person or not twins. After Identification, Performance of the process is measured

Person Identification Using Multimodal Biometrics under Different Challenges

The main aims of this chapter are to show the importance and role of human identification and recognition in the field of human-robot interaction, discuss the methods of person identification systems, namely traditional and biometrics systems, and compare the most commonly used biometric traits that are used in recognition systems such as face, ear, palmprint, iris, and speech. Then, by showing and comparing the requirements, advantages, disadvantages, recognition algorithms, challenges, and experimental results for each trait, the most suitable and efficient biometric trait for human-robot interaction will be discussed. The cases of human-robot interaction that require to use the unimodal biometric system and why the multimodal biometric system is also required will be discussed. Finally, two fusion methods for the multimodal biometric system will be presented and compared

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

Iris recognition using gabor filter / Zakhirulnizam Arshad

A biometric system provides automatic identification of a person based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. The iris recognition prototype process was started with an enrollment process where eye image will be process by performing automatic segmentation system that is based on the Hough transform. The segmentation process produced the extracted iris region from an eye and then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise biometric template and save it with require information. For identification process, eye image once again will be employed and process. The Hamming distance function was used for to find the matching between the two iris templates, and information of person will be displayed if both them found to match. Functionality testing shows that every functions in the system work and running well in enrollment process and also identification process. The result of accuracy test using 30 images show the matching rate of 57% of true match and 40% of false match. There are few limitations that can be improved for the future such as using hybrid Gabor Filter with any available feature extraction technique to eliminate noise and enhance the image. The prototype also can be improving by integrate it with the use of infra-red imaging device to capture the eye images in real life