scholarly journals A Technique of Fingerprint Recognition using Artificial Neural Network

2018 ◽  
Vol 8 (1) ◽  
pp. 1-6
Author(s):  
Soni , Saurabh Verma Soni , Saurabh Verma ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fingerprint Recognition ◽  
Artificial Neural

scholarly journals FINGERPRINT RECOGNITION USING ARTIFICIAL NEURAL NETWORK

2013 ◽  
pp. 62-65
Author(s):  
TRIPTI RANI BORAH ◽  
Kandarpa Kumar Sarma ◽  
PRAN HARI TALUKDAR
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Experimental Results ◽  
Fingerprint Recognition ◽  
Biometric Verification ◽  
Artificial Neural

Artificial Neural Network are different means of prediction, optimization and recognition. The ability of the ANN to learn given patterns makes them suitable for such applications. Fingerprint recognition is one such area that can be used as a means of biometric verification where the ANN can play a critical rule. An ANN can be configured and trained to handle such variations observed in the texture of the fingerprint. The specialty of the work is associated with the fact that if the ANN is configured properly it can tackle the variations in the fingerprint images and that way provides the insights for developing a system which requires these samples for verification and authorization. A system is designed to provide authentication decision using the fingerprint inputs can be a reliable means of verification. Such a system designed using ANN and using fingerprint inputs is described here. Experimental results show that the system is reliable enough for considering it as a part of a verification mechanism.

scholarly journals An Improved Biometric Fusion System Based on Fingerprint and Face using Optimized Artificial Neural Network

2019 ◽  
Vol 8 (11) ◽  
pp. 1568-1575 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition System ◽  
Conventional Technique ◽  
Optimization Techniques ◽  
Fingerprint Recognition ◽  
Fusion System ◽  
Bacterial Foraging Optimization ◽  
Artificial Neural ◽  
Biometric Fusion

This research presents an improved biometric fusion system (IBFS) that integrates fingerprint and face as a subsystem. Two authentication systems, namely, Improved Fingerprint Recognition System (IFPRS) and Improved Face Recognition System (IFRS), are introduced respectively. For both, Atmospheric Light Adjustment (ALA) algorithm is used as an image quality enhancement technique for the improvement in visualization of acquired fingerprint and face data. Genetic Algorithm (GA) is used as an optimization algorithm with minutiae feature for IFPRS and Speed Up Robust Feature (SURF) for IFRS. Artificial Neural Network (ANN) is used as a classifier for IBFS. For the demonstration of the results, quality based parameters are computed, and in the end, a comparison is drawn to depict the efficiency of the work.The optimization techniques such as Particle Swarm Optimization (PSO) and BFO (Bacterial Foraging Optimization) has been considered to determine the effectiveness of the proposed model.The experimental results consider different parameters such as False Acceptance Rate (FAR), False Rejection Ratio (FRR), Accuracy and Execution time which shows that performance of the proposed model better than the other optimization models. In addition, to enhance robustness of the proposed structure, the results further compared with conventional technique which shows that accuracy has been improved by 2%.

scholarly journals Automotive Start–Stop Engine Based on Fingerprint Recognition System

2019 ◽  
Vol 130 ◽  
pp. 01022
Author(s):  
Pranoko Rivandi ◽  
Astuti Winda ◽  
Dewanto Satrio ◽  
Mahmud Iwan Solihin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition System ◽  
Security System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Minutiae Extraction ◽  
Artificial Neural ◽  
The Difference ◽  
Artificial Neural Network Ann

Automated vehicle security system plays an important rule in nowadays advance automotive technology. One of the methods which can be applied for a security system is based on biometric identification system. Fingerprint recognition is one of the biometric systems that can be applied to the security system. In this work, fingerprint recognition system to start the motorcycle engine is developed. The fingerprint of the owner and other authorized persons will be stored into the database, then while the time of starting the engine of the vehicle, the fingerprint will be validated with the database. The minutiae extraction method is applied to find the difference of fingerprint each other after turn the image into grayscale and thinning. After the extraction, the next step is finding the ridge edge and bifurcation. The result of the image will be used as input to the Artificial Neural Network (ANN) to recognize authorized person only. The experiment of fingerprint recognition system shows that automatic start-stop engine using fingerprint recognition system based minutiae extraction and Artificial Neural Network (ANN) has accuracy 100 % and 100 %, respectively.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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