A Lyapunov Theory-Based Neural Network Approach for Face Recognition

2010 ◽  
pp. 23-48 ◽  
Author(s):  
Li-Minn Ang ◽  
King Hann Lim ◽  
Kah Phooi Seng ◽  
Siew Wen Chin
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Principal Component ◽  
Lyapunov Stability Theory ◽  
Recognition System ◽  
Lyapunov Theory ◽  
Neural Network Approach ◽  
Linear Discriminant ◽  
Feature Based ◽  
Definition Of

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

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.

DECISION-LEVEL FUSION OF PCA AND LDA-BASED FACE RECOGNITION ALGORITHMS

2006 ◽  
Vol 06 (02) ◽  
pp. 293-311 ◽  
Author(s):  
GIAN LUCA MARCIALIS ◽  
FABIO ROLI
Keyword(s):  
Face Recognition ◽  
Environmental Conditions ◽  
Principal Component ◽  
Environmental Parameters ◽  
Recognition System ◽  
Large Variability ◽  
Linear Discriminant ◽  
Decision Level ◽  
Recognition Algorithms ◽  
The Individual

In this paper, a face recognition system based on the fusion of two well-known appearance-based algorithms, namely Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), is proposed. Fusion is performed at the decision-level, that is, the outputs of the individual face recognition algorithms are combined. Two main benefits of such fusion are shown. First, the reduction of the dependence on the environmental conditions with respect to the best individual recognizer. Secondly, the overall performance improvement over the best individual recognizer. To this end, fusion is investigated under different environmental conditions, namely, "ideal" conditions, characterized by a very limited variability of environmental parameters, and "real" conditions with large variability of lighting and face expressions.

scholarly journals Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms

2016 ◽  
Vol 5 (2) ◽  
pp. 80
Author(s):  
Araoluwa Simileolu Filani ◽  
Adebayo Olusola Adetunmbi
Keyword(s):  
Face Recognition ◽  
Dimensional Reduction ◽  
Principal Component ◽  
Recognition System ◽  
Projection Methods ◽  
Nonlinear Methods ◽  
Linear Discriminant ◽  
Nonlinear Algorithms ◽  
Linear And Nonlinear ◽  
Pattern Information

This paper presents appearance based methods for face recognition using linear and nonlinear techniques. The linear algorithms used are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two nonlinear methods used are the Kernel Principal Components Analysis (KPCA) and Kernel Fisher Analysis (KFA). The linear dimensional reduction projection methods encode pattern information based on second order dependencies. The nonlinear methods are used to handle relationships among three or more pixels. In the final stage, Mahalinobis Cosine (MAHCOS) metric is used to define the similarity measure between two images after they have passed through the corresponding dimensional reduction techniques. The experiment showed that LDA and KFA have the highest performance of 93.33 % from the CMC and ROC results when used with Gabor wavelets. The overall result using 400 images of AT&T database showed that the performance of the linear and nonlinear algorithms can be affected by the number of classes of the images, preprocessing of images, and the number of face images of the test sets used for recognition.

Face Recognition Based on Artificial Neural Network: A Review

2021 ◽  
pp. 116-131
Author(s):  
Ameera Alblushi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Face Recognition ◽  
Recognition System ◽  
Neural Network Approach ◽  
Pattern Recognition Systems ◽  
The Face ◽  
Artificial Neural ◽  
Facial Recognition System

The face recognition/detection is considered as one of the most popular applications in the field of image processing and biometric pattern recognition systems. Although the face recognition approach improves authentication procedure, nevertheless still many challenges appear due to diversities in human facial expression, image huge size, background complexity, variation in illumination, poses, blurry, etc. Therefore, the face detection procedure is classified as one of the most difficult tasks in computer vision. This research paper tends to address the concept of image processing along with the use of the Artificial Neural Network approach and represent it is a potential capability in enhancing the method of extracting face pattern through an adaption of various ANN topologies. Furthermore, it represents fundamental phases associated with the construction of any facial recognition system. Finally, it provides a general overview of different literature survives that related to face recognition based on the use of different ANN approaches and algorithms

scholarly journals A Face Recognition System Based on Principal Component Analysis-Wavelet and Support Vector Machines

2019 ◽  
Vol 3 (2) ◽  
pp. 14-20
Author(s):  
Laith R. Fleah ◽  
Shaimaa A. Al-Aubi
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Face Image ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Face Recognition System

Face recognition can represent a key requirement in various types of applications such as human-computer interface, monitoring systems, as well as personal identification. In this paper, design and implement of face recognition system are introduced. In this system, a combination of principal component analysis (PCA) and wavelet feature extraction algorithms with support vector machine (SVM) and K-nearest neighborhood classifier is used. PCA and wavelet transform methods are used to extract features from face image using and identify the image of the face using SVMs classifier as well as the neural network are used as a classifier to compare its results with the proposed system. For a more comprehensive comparison, two face image databases (Yale and ORL) are used to test the performance of the system. Finally, the experimental results show the efficiency and reliability of face recognition system, and the results demonstrate accuracy on two databases indicated that the results enhancement 5% using the SVM classifier with polynomial Kernel function compared to use feedforward neural network classifier.

Sign in / Sign up

Export Citation Format

Share Document