scholarly journals DEVELOPMENT OF BIOMETRIC METHODS AND INFORMATION SECURITY TOOLS

2021 ◽  
Vol 2 (336) ◽  
pp. 121-124
Author(s):  
T. Zh. Mazakov ◽  
D. N. Narynbekovna
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Future Perspective ◽  
Data Set

Now a day’s security is a big issue, the whole world has been working on the face recognition techniques as face is used for the extraction of facial features. An analysis has been done of the commonly used face recognition techniques. This paper presents a system for the recognition of face for identification and verification purposes by using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) and the implementation of face recognition system is done by using neural network. The use of neural network is to produce an output pattern from input pattern. This system for facial recognition is implemented in MATLAB using neural networks toolbox. Back propagation Neural Network is multi-layered network in which weights are fixed but adjustment of weights can be done on the basis of sigmoidal function. This algorithm is a learning algorithm to train input and output data set. It also calculates how the error changes when weights are increased or decreased. This paper consists of background and future perspective of face recognition techniques and how these techniques can be improved.

scholarly journals Face Recognition Based on PCA Algorithm

2013 ◽  
pp. 79-83
Author(s):  
Taranpreet Singh Ruprah
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
The Face

This paper is proposed the face recognition method using PCA with neural network back error propagation learning algorithm .In this paper a feature is extracted using principal component analysis and then classification by creation of back propagation neural network. We run our algorithm for face recognition application using principal component analysis, neural network and also calculate its performance by using the photometric normalization technique: Histogram Equalization and comparing with Euclidean Distance, and Normalized correlation classifiers. The system produces promising results for face verification and face recognition. Demonstrate the recognition accuracy for given number of input pattern.

Short-Term Traffic Flow Forecasting of Road Based on Spline Weight Function Neural Networks

2014 ◽  
Vol 513-517 ◽  
pp. 695-698
Author(s):  
Dai Yuan Zhang ◽  
Jian Hui Zhan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Weight Function ◽  
Traffic Flow ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Forecasting Method

Traditional short-term traffic flow forecasting of road usually based on back propagation neural network, which has a low prediction accuracy and convergence speed. This paper introduces a spline weight function neural networks which has a feature that the weight function can well reflect sample information after training, thus propose a short-term traffic flow forecasting method base on the spline weight function neural network, specify the network learning algorithm, and make a comparative tests bases on the actual data. The result proves that in short-term traffic flow forecasting, the spline weight function neural network is more effective than traditional methods.

scholarly journals Text Dependent Speakers Pattern Classification with Back Propagation Neural Network

2019 ◽  
Vol 8 (4) ◽  
pp. 9139-9143
Keyword(s):  
Neural Network ◽  
Speaker Recognition ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Primary Objective ◽  
Primary Concern ◽  
Data Set ◽  
Speech Characteristics ◽  
Machine Interface

Speaker Recognition is the procedure of validating a speaker’s claimed identity using his/her speech characteristics which is unique to each individual. The primary objective of all speech recognition system is a man-machine interface which grants access into the system with the voice characteristics. This will served as a highly secure biometric system where security is the primary concern. The primary aim of this paper is to classify each speaker accurately with MFCC and Back Propagation Neural Network. Scaled conjugate gradient training function is used for back propagation neural network. A small database of 10 people is created from a group of five male and five female uttering the same sentence five times repeatedly. The sentence consists of five different words. The numbers of data set for classification is 22182.The accuracy obtained from the classification is 92.1% with small percentage of 7.9% misclassification which is acceptable good. The tool for simulation is MATLAB.

scholarly journals Implementation of Back Propagation Neural Network with PCA for Face Recognition

2019 ◽  
pp. 21-26
Author(s):  
Md. Manik Ahmed ◽  
A F M Zainul Abadin ◽  
Md. Anwar Hossain ◽  
Md. Imran Hossain
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network ◽  
Face Image ◽  
Identification System ◽  
Maximum Output

Face recognition is truly one of the demanding fields of biometric image processing system. Within this paper, we have implemented Back Propagation Neural Network for face recognition using MATLAB, where feature extraction and face identification system completely depend on Principal Component Analysis (PCA). Face images are multidimensional and variable data. Hence we cannot directly apply Back Propagation Neural Network to classify face without extracting the core area of face. So, the dimensionality of face image is reduced by the Principal Component Analysis algorithm then we have to explore unique feature for all stored database images called eigenfaces of eigenvectors. These unique features or eigenvectors are given as parallel input to the Back Propagation Neural Network (BPNN) for recognition of given test images. Here test image is taken from the integrated webcam which is applied to the BPNN trained network. The maximum output of the tested network gives the index of recognized face image. BPNN employing PCA is more robust and reliable than PCA based face recognition system.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

Sign in / Sign up

Export Citation Format

Share Document