scholarly journals AN ADAPTIVE ALGORITHM BASED AUTHENTICATION SYSTEM FOR REAL-TIME BIOMETRIC TIME-ATTENDANCE SYSTEM

2021 ◽  
Vol 9 (1) ◽  
pp. 77-90
Author(s):  
Thejaswini P., Anu H., D. Mahesh Kumar, Aravinda H. S.
Keyword(s):  
Real Time ◽  
Experimental Analysis ◽  
Adaptive Algorithm ◽  
Recognition Rate ◽  
Environmental Parameter ◽  
Biological Characteristics ◽  
Fingerprint Verification ◽  
Correction Technique ◽  
Authentication System ◽  
Biometric Features

Biometric features are physical and biological characteristics that are unique to a person and can be used to accomplish authentication based on the particular modality. The main purpose of implementation of an Adaptive auto-correction technique for biometric time-attendance system is to improve the matching rate of fingerprint verification under the condition when fingerprint patterns vary due to environmental parameter like temperature. An Adaptive auto-correction technique is proposed which auto-corrects the reference fingerprint template at the time of genuine user rejection. The proposed technique is implemented on commercially available biometric device which uses Innovatrics, a standard commercially available extractor and matcher. Evaluation is carried out on 250 fingerprint templates of 10-users captured at varying temperature from 250C to 00C. The experimental analysis will be carried out to improve the recognition rate.

scholarly journals Smart Driving License Verification System

2020 ◽  
Vol 5 (3) ◽  
pp. 218-224
Keyword(s):  
Embedded System ◽  
21St Century ◽  
Authentication Scheme ◽  
Biometric Authentication ◽  
Fingerprint Verification ◽  
Driving License ◽  
Verification System ◽  
License System ◽  
Authentication System ◽  
Biometric Features

The driving license system is very difficult to monitor via old conventional methods. The conventional verification system involves wastage of time and a chance to lead corruption. This project represents a biometricbased System for driving licensing verification. Biometrics is one of the most critical emerging technologies of the 21st century. From the various biometric features, Finger Print is one of the best. To keep the record of attendance report of students has become a very difficult work because sometimes their attendance may be recorded or missed. To deal with this problem i.e. to get the attendances recorded correctly we are using two different technologies viz. embedded system and biometrics. It is a very useful biometric-based driving license monitoring system that is easy and more suitable to monitor. Fingerprint verification is an important biometric technique for human identification. In this project, we represent a prototype automatic license authentication system. It uses fingerprints to verify the identity of an individual. In this project, we use the Fingerprint authentication scheme which is a non-imitable biometric authentication scheme. Using biometric authentication, we can try to avoid the non-licensed person from driving. While issuing the license, the specific person’s fingerprint is to be stored on the webserver along with its details. So at anywhere, the person should place on his finger on the fingerprint reader

scholarly journals Traceable Air Baggage Handling System Based on RFID Tags in the Airport

2008 ◽  
Vol 3 (1) ◽  
pp. 106-115 ◽  
Author(s):  
Ting Zhang ◽  
Yuanxin Ouyang ◽  
Yang He
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Recognition Rate ◽  
Cost Effective ◽  
Object Identification ◽  
Rfid Tags ◽  
Handling System ◽  
International Airport ◽  
Management Efficiency ◽  
Baggage Handling

The RFID is not only a feasible, novel, and cost-effective candidate for daily object identification but it is also considered as a significant tool to provide traceable visibility along different stages of the aviation supply chain. In the air baggage handing application, the RFID tags are used to enhance the ability for baggage tracking, dispatching and conveyance so as to improve the management efficiency and the users’ satisfaction. We surveyed current related work and introduce the IATA RP1740c protocol used for the standard to recognize the baggage tags. One distributed aviation baggage traceable application is designed based on the RFID networks. We describe the RFID-based baggage tracking experiment in the BCIA (Beijing Capital International Airport). In this experiment the tags are sealed in the printed baggage label and the RFID readers are fixed in the certain interested positions of the BHS in the Terminal 2. We measure the accurate recognition rate and monitor the baggage’s real-time situation on the monitor’s screen. Through the analysis of the measured results within two months we emphasize the advantage of the adoption of RFID tags in this high noisy BHS environment. The economical benefits achieved by the extensive deployment of RFID in the baggage handing system are also outlined.

IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

2018 ◽  
Vol 30 (03) ◽  
pp. 1850019
Author(s):  
Fatemeh Alimardani ◽  
Reza Boostani
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Rate ◽  
Acceptance Rate ◽  
Feature Reduction ◽  
Support Vector ◽  
Fingerprint Verification ◽  
Linear Discriminant ◽  
Comparative Results ◽  
Verification Systems

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

scholarly journals Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 555
Author(s):  
Jui-Sheng Chou ◽  
Chia-Hsuan Liu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Character Recognition ◽  
Initial Period ◽  
Control Point ◽  
Recognition Rate ◽  
Classification Model ◽  
License Plate ◽  
Construction Site ◽  
Illegal Mining

Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform

2013 ◽  
Vol 284-287 ◽  
pp. 2402-2406 ◽  
Author(s):  
Rong Choi Lee ◽  
King Chu Hung ◽  
Huan Sheng Wang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Real Time ◽  
Recognition Rate ◽  
Low Frequency ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
License Plate ◽  
License Plate Recognition ◽  
Image Pixels

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

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