Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

A COMPREHENSIVE REVIEW ON IRIS RECOGNITION METHODS

The necessity for the biometrical security has been increased in order to give security and safety from the theft, frauds, etc. Iris recognition acquired a considerable value amongst all the biometrics-based systems. It is utilized used for surveillance and authentication for detecting individuals and proving an individual’s identity. The present article discusses the various stages of recognizing iris images, which include acquiring, segmenting, normalizing, extracting features, and matching. The model of a typical iris recognition system of the eye is described and the results of its work are presented. The present study will investigate the comparative performances from various methods on the feature extraction for the accuracy of the iris recognition

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

IRDO: Iris Recognition by Fusion of DTCWT and OLBP

Iris Biometric is a physiological trait of human beings. In this paper, we propose Iris an Recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Over Lapping Local Binary Pattern (OLBP) Features. An eye is preprocessed to extract the iris part and obtain the Region of Interest (ROI) area from an iris. The complex wavelet features are extracted for region from the Iris DTCWT. OLBP is further applied on ROI to generate features of magnitude coefficients. The resultant features are generated by fusing DTCWT and OLBP using arithmetic addition. The Euclidean Distance (ED) is used to compare test iris with database iris features to identify a person. It is observed that the values of Total Success Rate (TSR) and Equal Error Rate (EER) are better in the case of proposed IRDO compared to the state-of-the art technique

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

A Survey of Iris Recognition System

The uniqueness of iris texture makes it one of the reliable physiological biometric traits compare to the other biometric traits. In this paper, we investigate a different level of fusion approach in iris image. Although, a number of iris recognition methods has been proposed in recent years, however most of them focus on the feature extraction and classification method. Less number of method focuses on the information fusion of iris images. Fusion is believed to produce a better discrimination power in the feature space, thus we conduct an analysis to investigate which fusion level is able to produce the best result for iris recognition system. Experimental analysis using CASIA dataset shows feature level fusion produce 99% recognition accuracy. The verification analysis shows the best result is GAR = 95% at the FRR = 0.1