Abstract-Image feature extraction is one of the basic works for biometric analysis. This paper presents the novel concept of application of ridgelets for iris recognition systems. Ridgelet transforms are the combination of Radon transforms and Wavelet transforms. They are suitable for extracting the abundantly present textural data that is in an iris. The technique proposed here uses the ridgelets to form an iris signature and to represent the iris. This paper contributes towards creating an improved iris recognition system. There is a reduction in the feature vector size, which is 1X4 in size. The False Acceptance Rate (FAR) and False Rejection Rate (FRR) were also reduced and the accuracy increased. The proposed method also avoids the iris normalization process that is traditionally used in iris recognition systems. Experimental results indicate that the proposed method achieves an accuracy of 99.82%, 0.1309% FAR, and 0.0434% FRR.
This paper proposes the utility of texture and color for iris recognition systems. It contributes for improvement of system accuracy with reduced feature vector size of just 1 Â 3 and reduction of false acceptance rate (FAR) and false rejection rate (FRR). It avoids the iris normalization process used traditionally in iris recognition systems. Proposed method is compared with the existing methods. Experimental results indicate that the proposed method using only color achieves 99.9993 accuracy, 0.0160 FAR, and 0.0813 FRR. Computational time e±ciency achieved is of 947.7 ms.
Security has been an issue these days. Every nation all over the world is very much concerned about its data security says cables obtained by WikiLeaks. Generally, one can identify oneself with a system by three basic methods based on Knowledge (what you know), Possession (what you have), and Reality (who you are). Both knowledge (passwords, PINs etc) and possession (e-tokens, ID cards etc) based methods are theft prone. Hence only the third type reality based (biometrics) are the options to rely on. Biometrics classified as physiological and behavioural traits are in use. Physiological traits like facial features, voice patterns, hand geometry, retinal patters, vein patterns, facial thermography, DNA matching, nailbed identification, ear shape recognition, finger prints and behavioural traits like signature dynamics, voice verification, gait analysis, keystroke dynamics etc all explored as biometric identifiers with varying levels of success. However, they have their own limitations. Nevertheless, iris has unique patterns, as it is a fact that no two-iris patterns are alike. That is one cannot be enrolled with the right eye and authenticated with the left. Uniqueness of iris motivates oneself to sustain it as a biometric authentication technique. After rigorous review of hundreds of papers on iris recognition systems, the authors are presenting this chapter. It contributes for the recent trends in iris recognition methodologies. IntroductionAn ocean of information with unlimited expanse and unfathomable depth got created and made accessible to all human beings through huge databases. Even if the ocean of information is available, only a small drop of this ocean, which is appropriate for the specific event, is the requirement of the day. Thus in the vast ocean of available databases picking the drop that quenches our thirst is the current field of research as the level of ocean of database is increasing day by day. The curse of e-wars in all the e-systems is calling for more and more secure systems and protection of data. At the same time, the matter of border security has become a matter of concern. Hence, the researchers have focused their attention on the secure biometric systems. Biometrics like the one fingerprints [1 -2], facial features [3], voice patterns, hand geometry, retinal patters, vein patterns, signature dynamics, iris etc
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