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

Iris feature extraction: a survey

Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system

Palmprint identification using restricted fusion

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Face Recognition Technique Using Gabor Wavelets And Singular Value Decomposition

Gabor Wavelets (GWs) (also known as Gabor filter) and Singular Value Decomposition (SVD) have been studied extensively in the area of face recognition. In this project, face recognition system is developed using combination of GWs and SVD. Both techniques are used to extract facial features from the human facial image and presented in the form of feature vector. For GWs, only 12 out of 40 GWs are selected to extract facial features from the facial images. This offers the advantage of reducing computational time of feature extraction. As for SVD, only the first five singular values are selected and its associated right singular vectors are used as the facial feature vectors. The use of SVD in addition to the GWs increases the reliability of the face recognition system. In the face verification and matching stage, the similarity level between facial images is determined by computing the distance between the resulting facial feature vectors obtained from GWs and SVD respectively. Overall, the Gabor-SVD based face recognition technique showed constructive and promising result in recognizing the valid user and rejecting invalid users on the JAFFE database

FACE RECOGNITION METHOD USING GABOR WAVELETS (GWS)

Facial features can be used to recognize and identify the characteristics of a person. In this project, Gabor Wavelet (GW) based recognition technique is proposed whereby the GW is used to extract the facial feature of a person. The face recognition system consist of four (4) major stages namely image preprocessing, feature extraction, matching technique and classification technique. In feature extraction stage, the input images are converted into grayscale image prior to applying the 2D GWs. The resulting feature vectors are used to test the similarity score with the feature vectors of the facial image in the database

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Two-Dimensional Face Recognition Algorithms in the Frequency Domain

ABSTRACT Two-Dimensional Face Recognition Algorithms in the Frequency Domain Alper Serhat Zeytunlu The importance of security, law-enforcement and identity verification has necessitated the development of automated stable, fast and highly accurate algorithms for human recognition. Face recognition is one of the most popular techniques used for these purposes. Face recognition algorithms are performed on very large size of datasets obtained under various challenging conditions. Principal component analysis (PCA) is a widely used technique for face recognition. However, it has major drawbacks of (i) losing the image details due to the transformation of two-dimensional face images into one-dimensional vectors, (ii) having a large time complexity due to the use of a large size covariance matrix and (iii) suffering from the adverse effect of intra-class pose variations resulting in reduced recognition accuracy. To overcome the problem of intra-class pose variations, Fourier magnitudes have been used for feature extraction in the PCA algorithm giving rise to the so called FM-PCA algorithm. However, the time complexity of this algorithm is even higher. On the other hand, to address the other two drawbacks of the PCA algorithm, two-dimensional PCA (2DPCA) algorithms have been proposed. This thesis is concerned with developing 2DPCA algorithms that incorporate the advantages of FM-PCA in improving the accuracy and that of 2DPCA algorithms in improving the accuracy as well as the time complexity. Towards this goal, 2DPCA algorithms, referred to as the FM-r2DPCA and FM-(2D)2PCA algorithms, that use Fourier-magnitudes rather than the raw pixel values, are first developed. Extensive simulations are conducted to demonstrate the effectiveness of using the Fourier-magnitudes in providing higher recognition accuracy over their spatial domain counterparts. Next, by taking advantage of the energy compaction property of the Fourier-magnitudes, the proposed algorithms are further developed to significantly reduce their computational complexities with little loss in the recognition accuracy. Simulation results are provided to validate this claim

A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.

Human face recognition has received a wide range of attention since 1990s. Recent approaches focus on a combination of dimensionality reduction-based feature extraction algorithms and various types of classifiers. This thesis provides an in depth comparative analysis of neural and statistical classifiers by combining them with existing dimensionality reduction-based algorithms. A set of unified face recognition systems were established for evaluating alternate combinations in terms of recognition performance, processing time, and conditions to achieve certain performance levels. A preprocessing system and four dimensionality reduction-based methods based on Principal Component Analysis (PCA), Two-dimensional PCA, Fisher\u27s Linear Discriminant and Laplacianfaces were utilized and implemented. Classification was achieved by using various types of classifiers including Euclidean Distance, MLP neural network, K-nearest-neighborhood classifier and Fuzzy K-Nearest Neighbor classifier. The statistical model is relatively simple and requires less computation complexity and storage. Experimental results were shown after the algorithms were tested on two databases of known individuals, Yale and AR database. After comparing these algorithms in every aspect, the results of the simulations showed that considering recognition rates, generalization ability, classification performance, the power of noise immunity and processing time, the best results were obtained with the Laplacianfaces, using either Fuzzy K-NN.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .X86. Source: Masters Abstracts International, Volume: 45-01, page: 0428. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006