Multimodal biometric authentication algorithm at score level fusion using hybrid optimization

Biometric is emerging technology in identification and authentication of human being with more reliable and accurate. Combining multiple biometric systems is a promising solution to provide more security. It eliminates the disadvantages of unimodal biometric systems such as non-universality, noise in sensed data, intra-class variations, distinctiveness, spoof attacks and traditional method of authenticating a human and their identity. The proposed method depicts a multimodal biometric algorithm is designed to recognize individuals for robust and secured authentication using normalized score level fusion techniques with hybrid Genetic Algorithm and Particle Swarm Optimization for optimization in order to reduce False Acceptance Rate and False Rejection Rate and to enhance Equal Error Rate and Accuracy.

An Improved Iris Recognition System with Template Security using CT and SVD

The Iris biometric system is the most prominent method for identification of individual. Many researchers have been presented iris recognition methods from decade but a fully suitable solution for real world scenario is not implemented yet. The two major issues are responsible for it. First is no accurate method to operate on non-ideal iris images with high recognition rate. Second one is deployment of system with high security on the existing real world situations. In this Paper, the above mentioned problems are solved to an extent. An accurate and secured iris template encoding method is used for generate highly secured encoded binary pattern for iris template. Contourlet transform and Singular Value decomposition is used for this purpose. Beside this security feature, the proposed method used best combinations of algorithm for provide high accuracy as compared to conventional system of iris recognition. In Our approach IIT Delhi iris database is used as input image. Iris region from eye image is extracted by canny edge detection and Hough transforms to achieve high recognition rate. Daugman’s rubber sheet model is used for normalization. Security for normalized template is provided by Contourlet transform and Singular Value Decomposition. At last stage the combination of Hamming Distance and Normalized Correlation coefficient is used to achieve high recognition rate. So at each stage of iris recognition system all methods and algorithms are performed very well and provide higher accuracy as compared to existing iris recognition system

An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster

Performance analysis of multimodal biometric fusion

Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.State of Kuwait – The Public Authority of Applied Education and Trainin