An enhanced iris recognition and authentication system using energy measure

In order to fight identity fraud, the use of a reliable personal identifier has become a necessity. Using Personal Identification Number (PIN) or a password is no longer secure enough to identify an individual. Iris recognition is considered to be one of the best and accurate form of biometric measurements compared to others, it has become an interesting research area. Iris recognition and authentication has a major issue in its code generation and verification accuracy, in order to enhance the authentication process, a binary bit sequence of iris is generated, which contain several vital information that is used to calculate the Mean Energy and Maximum Energy that goes into the eye with an adopted Threshold Value. The information generated can further be used to find out different eye ailments. An iris is obtained using a predefined iris image which is scanned through eight (8) different stages and wavelet packet decomposition is used to generate 64 wavelet packages bit iris code so as to match the iris codes with Hamming distance criteria and evaluate different energy values. The system showed 98% True Acceptance Rate and 1% False Rejection Rate and this is because some of the irises weren’t properly captured during iris acquisition phase. The system is implemented using UBIRIS v.1 Database.Keywords: Local Image Properties, Authentication Enhancement, Iris Authentication, Local Image, Iris Recognition, Binary Bit Sequenc

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

Biometric iris image segmentation and feature extraction for iris recognition

PhD ThesisThe continued threat to security in our interconnected world today begs for urgent solution. Iris biometric like many other biometric systems provides an alternative solution to this lingering problem. Although, iris recognition have been extensively studied, it is nevertheless, not a fully solved problem which is the factor inhibiting its implementation in real world situations today. There exists three main problems facing the existing iris recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in real world situation. In this thesis, six novel approaches were derived and implemented to address these current limitation of existing iris recognition systems. A novel fast and accurate segmentation approach based on the combination of graph-cut optimization and active contour model is proposed to define the irregular boundaries of the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final irregular boundary of the pupil/iris is refined and segmented using graph-cut based active contour (GCBAC) model proposed in this work. The segmentation is performed in two levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing technique based on adaptive weighted edge detection and high-pass filtering is used to detect reflections on the high intensity areas of the image while exemplar based image inpainting is used to eliminate the reflections. After the segmentation of the iris boundaries, a post-processing operation based on combination of block classification method and statistical prediction approach is used to detect any super-imposed occluding eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber sheet model. In the second stage, an approach based on construction of complex wavelet filters and rotation of the filters to the direction of the principal texture direction is used for the extraction of important iris information while a modified particle swam optimization (PSO) is used to select the most prominent iris features for iris encoding. Classification of the iriscode is performed using adaptive support vector machines (ASVM). Experimental results demonstrate that the proposed approach achieves accuracy of 98.99% and is computationally about 2 times faster than the best existing approach.Ebonyi State University and Education Task Fund, Nigeri