Anatomization of the systems of dimension relaxation for facial recognition

2020 ◽  
pp. 1-11
Author(s):  
Mayamin Hamid Raha ◽  
Tonmoay Deb ◽  
Mahieyin Rahmun ◽  
Tim Chen
Keyword(s):  
Face Recognition ◽  
Main Idea ◽  
Principal Component ◽  
Feature Space ◽  
Projection Methods ◽  
Data Representation ◽  
Linear Discriminant ◽  
Class Differences ◽  
Underlying Space ◽  
Maximum Variance

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.

scholarly journals An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Huang ◽  
Kehua Su ◽  
Jamal El-Den ◽  
Tao Hu ◽  
Junlong Li
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Feature Space ◽  
Recognition Algorithm ◽  
Linear Discriminant ◽  
Face Images ◽  
Sample Set ◽  
Multilinear Principal Component Analysis

We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while theKnearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

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.

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.

AN ADAPTIVE FACE RECOGNITION IN COMBINED GLOBAL AND LOCAL PRESERVING FEATURE SPACE

2011 ◽  
Vol 25 (01) ◽  
pp. 99-115 ◽  
Author(s):  
K. RUBA SOUNDAR ◽  
K. MURUGESAN
Keyword(s):  
Face Recognition ◽  
Minimum Distance ◽  
Feature Space ◽  
Threshold Value ◽  
Training Phase ◽  
Facial Image ◽  
Maximum Variance ◽  
Feature Spaces ◽  
Fair Comparison ◽  
Global And Local

Computer face recognition promises to be a powerful tool and is becoming important in our security-heightened world. Several research works on face recognition based on appearance, features like intensity, color, textures or shape have been done over the last decade. In those works, mostly the classification is achieved by finding the minimum distance or maximum variance among the training and testing feature set. This leads to the wrong classification when presenting the untrained image or unknown image, since the classification process locates at least one winning cluster having minimum distance or maximum variance among the existing clusters. But for the security related applications, these new facial image should be reported and necessary action has to be taken accordingly. In this paper, we propose the following two techniques for this purpose: (i) Use a threshold value calculated by finding the average of the minimum matching distances of the wrong classifications encountered during the training phase. (ii) Use the fact that the wrong classification increases the ratio of within-class distance and between-class distance. Experiments have been conducted using the ORL facial database and a fair comparison is made with the conventional feature spaces to show the efficiency of these techniques.

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.

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