scholarly journals Iris Identification Based on the Fusion of Multiple Methods

2021 ◽  
pp. 1364-1375
Author(s):  
Asaad Noori Hashim ◽  
Roaa Razaq Al-Khalidy
Keyword(s):  
Iris Recognition ◽  
Local Binary Patterns ◽  
Histogram Equalization ◽  
Recognition System ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Scale Invariant Feature ◽  
City Block Distance ◽  
Iris Identification ◽  
The City

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.

A Contactless Palm Vein Recognition System Based on Principle Line SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3469-3472 ◽  
Author(s):  
Wei Wu ◽  
Sen Lin ◽  
Hui Song
Keyword(s):  
Traditional Method ◽  
Recognition System ◽  
Scale Invariant ◽  
Limited Effect ◽  
Image Deformation ◽  
Invariant Feature ◽  
Feature Transform ◽  
Vein Recognition ◽  
Palm Vein ◽  
Scale Invariant Feature

Compared with the traditional method of contact collection, contactless acquisition is the mainstream and trend of palm vein recognition. However, this method may lead to image deformation caused by no parallel of the palm plane and the sensor plane. In order to improve the limited effect of Scale Invariant Feature Transform (SIFT) about this problem, a better method of palm vein recognition which based on principle line SIFT is proposed. Based on the self-built database, this method is compared with the SIFT and other typical palm vein recognition methods, the experimental results show that our system can achieve the best performance.

scholarly journals Dog recognition in public places based on convolutional neural network

2019 ◽  
Vol 15 (5) ◽  
pp. 155014771982967
Author(s):  
Jianquan Ouyang ◽  
Hao He ◽  
Yi He ◽  
Huanrong Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Public Places ◽  
Invariant Feature ◽  
Channel Information ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The City

With the increase in the number of dogs in the city, the dogs can be seen everywhere in public places. At the same time, more and more stray dogs appear in public places where dogs are prohibited, which has a certain impact on the city environment and personal safety. In view of this, we propose a novel algorithm that combines dense–scale invariant feature transform and convolutional neural network to solve dog recognition problems in public places. First, the image is divided into several grids; then, the dense–scale invariant feature transform algorithm is used to split and combine the descriptors, and the channel information of the eight directions of the image is extracted as the input of the convolutional neural network; and finally, we design a convolutional neural network based on Adam optimization algorithm and cross-entropy to identify the dog species. The experimental results show that the algorithm can fully combine the advantages of dense–scale invariant feature transform and convolutional neural network to achieve dog recognition in public places, and the correct rate is 94.2%.

scholarly journals Multifactor Authentication in Automated Teller Machine

2021 ◽  
Vol 9 (12) ◽  
pp. 1780-1783
Author(s):  
Vigneshwar Muriki
Keyword(s):  
Iris Recognition ◽  
Fingerprint Recognition ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Multifactor Authentication ◽  
Automated Teller Machine ◽  
Small Device ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Primary Problem

Abstract: Skimming of card details is the primary problem faced by many people in today’s world. This can be done in many ways. For instance, a thief can insert a small device into the machine and steal the information. When a person swipes or inserts a card, the details will be captured and stored. This problem can be solved using biometrics. Biometrics include fingerprint, iris, face, retina scanning, etc. This paper focused on solving this issue using fingerprint and iris recognition using OpenCV and propose a suitable method for this issue. Fingerprint and iris recognition are performed by identifying the keypoints and descriptors and matching those with the test data. Keywords: Biometrics, Fingerprint recognition, Iris recognition, Scale Invariant Feature Transform, Oriented FAST and Rotated BRIEF, OpenCV

scholarly journals Human – Ear Recognition Using Scale Invariant Feature Transform

2021 ◽  
pp. 1-12
Author(s):  
Abbas Hassin ◽  
Dheyaa Abbood
Keyword(s):  
Distance Measure ◽  
Recognition System ◽  
Scale Invariant Feature Transform ◽  
Morphological Operations ◽  
Scale Invariant ◽  
Ear Recognition ◽  
Invariant Feature ◽  
Feature Transform ◽  
Human Ear ◽  
Scale Invariant Feature

Biometrics techniques are the standard of a wide group of many applications for a human’s identification and verification issues. Because of this reason, a high scale of security needs to search for a new way to identify the person arises. In this paper, establish a human ear recognition system is proposed. This system combines four main phases: ear detection, ear feature extraction, ear recognition, and confirmation. The essential of the proposed system is to divide the ear image into the skin and non-skin pixels using a likelihood skin detector. The likelihood image processes by morphological operations to complete ear regions.  Scale-invariant feature transform uses for extracting the fixed features of the ear. Ear recognition includes two modes identification mode and verification mode. Euclidean Distance Measure (EDM) uses for similarity measure between the first image in the database and a new image. According to the three experiments conducted in this paper, the results of the different datasets, the accuracy ratio are 100%, 92%.and 92% respectively.

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