Ordinal Measure of Discrete Cosine Transform Blocks for Iris Identification

Currently, a common method for identifying a person is by means of an identitycard (ID) or combination of an ID and password. The approaches are not very reliable, since the ID can be stolen and password can be forgotten. A more reliable identification system is required. In the last decades, identification systems based on biometrics have been gaining attention, since they are more reliable. Biometrics-based devices identify people based on their physical or psychological characteristics, such as palmprints, fingerprints, gait and iris. Unlike fingerprints or palmprints, irides features distribute randomly, and the features were unique; the features between right and left eyes aredifferent, as well as between twins. Therefore, in addition to reliability, the use of irides can enhance identification accuracy. Purpose of the paper was to improve identification rate of an iris identification method, using ordinal measure of Discrete Cosine Transform (DCT) coefficient. The input iris image was tiled into blocks of 8x8 pixels, then the DCT was applied to each blocks. The AC coefficients of each block were sorted from the smallest to the largest values, in which the sorted values were referred to as ordinal measures.Identification was accomplished by measuring a distance between the ordinal measure of the input images with the ones of the existing images in the database using Minkwoski distance metric. Proposed method increased the averaged identification rate as compared to the previous method by nearly twice from 33% to 61.4%

Combining multiple Iris matchers using advanced fusion techniques to enhance Iris matching performance

M.Phil. (Electrical And Electronic Engineering)The enormous increase in technology advancement and the need to secure information e ectively has led to the development and implementation of iris image acquisition technologies for automated iris recognition systems. The iris biometric is gaining popularity and is becoming a reliable and a robust modality for future biometric security. Its wide application can be extended to biometric security areas such as national ID cards, banking systems such as ATM, e-commerce, biometric passports but not applicable in forensic investigations. Iris recognition has gained valuable attention in biometric research due to the uniqueness of its textures and its high recognition rates when employed on high biometric security areas. Identity veri cation for individuals becomes a challenging task when it has to be automated with a high accuracy and robustness against spoo ng attacks and repudiation. Current recognition systems are highly a ected by noise as a result of segmentation failure, and this noise factors increase the biometric error rates such as; the FAR and the FRR. This dissertation reports an investigation of score level fusion methods which can be used to enhance iris matching performance. The fusion methods implemented in this project includes, simple sum rule, weighted sum rule fusion, minimum score and an adaptive weighted sum rule. The proposed approach uses an adaptive fusion which maps feature quality scores with the matcher. The fused scores were generated from four various iris matchers namely; the NHD matcher, the WED matcher, the WHD matcher and the POC matcher. To ensure homogeneity of matching scores before fusion, raw scores were normalized using the tanh-estimators method, because it is e cient and robust against outliers. The results were tested against two publicly available databases; namely, CASIA and UBIRIS using two statistical and biometric system measurements namely the AUC and the EER. The results of these two measures gives the AUC = 99:36% for CASIA left images, the AUC = 99:18% for CASIA right images, the AUC = 99:59% for UBIRIS database and the Equal Error Rate (EER) of 0.041 for CASIA left images, the EER = 0:087 for CASIA right images and with the EER = 0:038 for UBIRIS images

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network