A State of Art for Survey of Combined Iris and Fingerprint Recognition Systems

Biometrics is developing into a technological science in this lifelong technology for the defense of identification. Biometrics is the technology to recognize individuals based on facial features, fingerprints, iris, retina, speech, handprints, etc. Biometric features are used for human recognition and identification. Much research was done in the last years on the biometric system because of a growing need for identification methods. This paper offers an overview of biometric solutions using fingerprint and iris identification, their uses, and Compare the data set, methods, Fusion Level, and the accuracy of the results.

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.

Iris Identification Based on the Fusion of Multiple Methods

Iris recognition occupies an important rank among the biometric types of approaches as a result of its accuracy and efficiency. The aim of this paper is to suggest a developed system for iris identification based on the fusion of scale invariant feature transforms (SIFT) along with local binary patterns of features extraction. Several steps have been applied. Firstly, any image type was converted to grayscale. Secondly, localization of the iris was achieved using circular Hough transform. Thirdly, the normalization to convert the polar value to Cartesian using Daugman’s rubber sheet models, followed by histogram equalization to enhance the iris region. Finally, the features were extracted by utilizing the scale invariant feature transformation and local binary pattern. Some sigma and threshold values were used for feature extraction, which achieved the highest rate of recognition. The programming was implemented by using MATLAB 2013. The matching was performed by applying the city block distance. The iris recognition system was built with the use of iris images for 30 individuals in the CASIA v4. 0 database. Every individual has 20 captures for left and right, with a total of 600 pictures. The main findings showed that the values of recognition rates in the proposed system are 98.67% for left eyes and 96.66% for right eyes, among thirty subjects.

Second-Order Statistical Methods GLCM for Authentication Systems

For many uses, biometric systems have gained considerable attention. Iris identification was One of the most powerful sophisticated biometrical techniques for effective and confident authentication. The current iris identification system offers accurate and reliable results based on near-infrared light (NIR) images when images are taken in a restricted area with fixed-distance user cooperation. However, for the color eye images obtained under visible wavelength (VW) without collaboration among the users, the efficiency of iris recognition degrades because of noise such as eye blurring images, eye lashing, occlusion, and reflection. This work aims to use the Gray-Level Co- occurrence Matrix (GLCM) to retrieve the iris's characteristics in both NIR iris images and visible spectrum. GLCM is second-order Statistical-Based Methods for Texture Analysis. The GLCM-based extraction technology was applied after the preprocessing method to extract the pure iris region's characteristics. The Energy, Entropy, Correlation, homogeneity, and Contrast collection of second-order statistical features are determined from the generated co-occurrence matrix, Stored as a vector for numerical features. This approach is used and evaluated on the CASIA v1and ITTD v1 databases as NIR iris image and UBIRIS v1 as a color image. The results showed a high accuracy rate (99.2 %) on CASIA v1, (99.4) on ITTD v1, and (87%) on UBIRIS v1 evaluated by comparing to the other methods

Various Strategies and Procedures Utilized for Gender Acknowledgment Utilizing Voice and Its Highlights

As discourse is viewed as one of the significant pieces of the people, as it is supportive for understanding the feelings and sentiments and so forth of the individual talking. The bio measurements innovation these days are particularly mainstream like unique mark and so forth. However, the upgrade of this innovation has additionally come like face location, iris identification, and voice recognition and so on. Voice acknowledgment is utilized for the sexual orientation recognizable proof also in light of the fact that it is been considered as one the dependable method of sex distinguishing proof. Voice acknowledgment is fundamentally understanding the voice signals and convert them into little examples and the machine is been will be been prepared to recognize those examples. The paper tells about the voice acknowledgment and the different strategy utilized for it