Sift Algorithm for Iris Feature Extraction

Iris recognition is proving to be one of the most reliable biometric traits for personal identification In fact iris patterns have stable invariant and distinctive features for personal identification Reliable authorization and authentication are becoming necessary for many everyday applications Iris recognition has been paid more attention due to its high reliability in personal identification But iris feature extraction is easily affected by some practical factors such as inaccurate localization occlusion and nonlinear elastic deformation The objective of the study and proposed work is to adapt the increasing usage of biometric systems which can reduce the iris preprocessing and describe iris local properties effectively and have encouraging iris recognition performance This work presents an efficient algorithm of iris feature extraction based on modified scale invariant feature transform algorithm SIF

Iris Feature Extraction and Recognition Based on Wavelet-Based Contourlet Transform

AbstractIn view of the limitation of poor direction selectivity about 2-D wavelet transform and the problem of redundancy on contourlet transform, an iris texture feature extraction method based on wavelet-based contourlet transform (WBCT)for obtaining high quality features is proposed in the paper. Firstly, the preprocessed iris image is decomposed by WBCT, then calculating its energy, mean, standard deviation and Hu invariant moments of each subband of different scales and different directions, and taking them as the eigenvalues of iris image, finally, it tests on four iris image databases by using Euclidean distance. Experimental results show that the algorithm is simple and effective, and obtain better recognition performance

Human Iris Recognition for Clean Electoral Process in India by Creating a Fraud Free Voter Registration List

AbstractHuman Iris pattern matching and recognition system is considered to be the best biometric identification found so far because of the unique features found in the iris and moreover the textured patterns of iris remain stable, invariant and distinct throughout the whole life. Iris recognition techniques involve a mathematical analysis of the unique stable patterns that are structured within the iris and then the comparison is being done with an already existing database. In this paper the implementation of creating a fraud free voter ID list is being done as to make a clean Electoral environment. For this localization of Iris and Pupils are done by canny edge detection algorithm, Normalization is done by Dougman's Normalization method and feature extraction is being done using Log Gabor Filter and lastly method of matching is accomplished by Euclidian distance1, 2. MATLAB 2011 version is used for developing the present study, and much of the emphasis is given on software for Recognition of Irises in an efficient manner

EXTRACTING RULES FROM TRAINED RBF NEURAL NETWORKS

This paper describes a method of rule extraction from trained artificial neural networks. The statement of the problem is given. The aim of rule extraction procedure and suitable neural networks for rule extraction are outlined. The RULEX rule extraction algorithm is discussed that is based on the radial basis function (RBF) neural network. The extracted rules can help discover and analyze the rule set hidden in data sets. The paper contains an implementation example, which is shown through standalone IRIS data set

An Improved Bovine Iris Segmentation Method

In order to improve the performance of bovine iris image segmentation, an improved iris image segmentation algorithm is proposed according to the characteristics of bovine iris image. Firstly, based on mathematical morphology and noise suppression template, the inner and outer edges of bovine iris are detected by dynamic contour tracking and least squares fitting ellipse respectively. Then, the annular iris region is normalized. Finally, the normalized iris image is enhanced with adaptive image enhancement method. The experimental results show that the algorithm can effectively segment iris region, it has good performance of speed and accuracy for iris segmentation, and can eliminate the effects of uneven illumination, iris shrinkage and rotation, it promotes iris feature extraction and matching, which has certain reference significance for iris recognition research and meat food safety management of large livestock

Personal Authentication System Based Iris Recognition with Digital Signature Technology

Authentication based on biometrics is being used to prevent physical access to high-security institutions. Recently, due to the rapid rise of information system technologies, Biometrics are now being used in applications for accessing databases and commercial workflow systems. These applications need to implement measures to counter security threats.  Many developers are exploring and developing novel authentication techniques to prevent these attacks. However, the most difficult problem is how to keep biometric data while maintaining the practical performance of identity verification systems. This paper presents a biometrics-based personal authentication system in which a smart card, a Public Key Infrastructure (PKI), and iris verification technologies are combined. Raspberry Pi 4 Model B+ is used as the core of hardware components with an IR Camera. Following that idea, we designed an optimal image processing algorithm in OpenCV/ Python, Keras, and sci-kit learn libraries for feature extraction and recognition is chosen for application development in this project. The implemented system gives an accuracy of (97% and 100%) for the left and right (NTU) iris datasets respectively after training. Later, the person verification based on the iris feature is performed to verify the claimed identity and examine the system authentication. The time of key generation, Signature, and Verification is 5.17sec,0.288, and 0.056 respectively for the NTU iris dataset. This work offers the realistic architecture to implement identity-based cryptography with biometrics using the RSA algorithm