Iris Image Recognition using Optimized Kohonen Self Organizing Neural Network

The pursuit to develop an effective people management system has widened over the years to manage the enormous increase in population. Any management system includes identification, verification and recognition stages. Iris recognition has become notable biometrics to support the management system due to its versatility and non-invasive approach. These systems help to identify the individual with the texture information distributed around the iris region. Many classification algorithms are available to help in iris recognition. But those are very sophisticated and require heavy computation. In this paper, an improved Kohonen self-organizing neural network (KSONN) is used to boost the performance of existing KSONN. This improvement is brought by the introduction of optimization technique into the learning phase of the KSONN. The proposed method shows improved accuracy of the recognition. Moreover, it also reduces the iterations required to train the network. From the experimental results, it is observed that the proposed method achieves a maximum accuracy of 98% in 85 iterations

An Iris Recognition System Based on Angular Radial Partitioning and Statistical Texture Analysis with Sum & Difference Histogram

Iris based identification systems are considered among the most promising recognition systems due to the inner characteristics of the iris, such as uniqueness, stability and time invariance. This paper proposes a new texture based iris recognition system based on Angular Radial Partitioning (ARP) and Sum & Difference Histogram (SDH). After the iris segmentation step, ARP is used to divide the iris’s texture into sectors, SDH allows for the production of probability vectors, which are then used to extract statistical features. Finally, classification is performed with the K-Nearest Neighbour algorithm. Experimental results on the Ubiris and Upol databases testify the superior performance of the proposed approach, which can handle the presence of eyelids and eyelashes, as well as partially occluded irises and out of focus images. In all experiments the accuracy of the our system is around 97% also when the training set is made up of only two pictures per class, and the corresponding low percentage of FAR suggests that the proposed approach is a good prototype for biometric recognition systems run in identification mode

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

Iris Recognition System using Gabor Filter & Edge Detection

Biometric identification is the recognition of an individual by means of their physical or behavioral characteristics. Iris recognition in past decade has become one of reliable & unique biometric identification. In this research the various iris recognition method such as segmentation & normalization are applied on the eye image of 320x280 dimensions is obtained from CASIA database. 1-D Gabor Filter is used for feature extraction for eyelid detection & enhancing the segmented iris image. Edge detection techniques such as canny, sobel & prewitt are used for obtaining the fine edges. Performance is calculated on various evaluation parameters such as PSNR & MSE value for these edge detection techniques. Result shows sobel has less PSNR & showing more accurate & better result as compare to canny & prewitt. Matching is done using hamming distance by calculating the bit difference of processed or original image

Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system

An image processing pipeline to segment iris for unconstrained cow identification system

One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; LuxemburgoFil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentin

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Cumulative and Ratio Time Evaluations in Keystroke Dynamics To Improve the Password Security Mechanism

The password mechanism is widely adopted as a control security system to legitimate access to a database or a transaction content or computing resources. This is because of the low cost of the mechanism, the software routine simplicity, and the facility for the user. But the password mechanism can suffer from serious vulnerabilities, which have to be reduced in some way. An aid comes from the keystroke dynamic evaluation, which uses the rhythm in which an individual types characters on a keyboard. It has been demonstrated how the keystroke dynamics are unique biometric template of the users typing pattern. So, the dwell time (the time a key pressed) and the flight time (the time between “key up” and the next “key down”) are used to verify the real user’s identity. In this work we investigated the keystroke dynamic already reported in literature but with some differences, so to obtain additional benefits. Rather than the commonly adopted absolute times (dwell and fly times), we deal with cumulative and ratio ones (i.e. sum and ratio of dwell and fly times), taking into account that the latest are times which do not change even if the user’s typing style evolves with practic