scholarly journals Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhangjing Yang ◽  
Chuancai Liu ◽  
Pu Huang ◽  
Jianjun Qian
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
Membership Degree ◽  
Extraction Techniques ◽  
Fisher Criterion ◽  
Extraction Algorithm ◽  
Definition Of

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzyk-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

scholarly journals Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

2017 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Fandiansyah Fandiansyah ◽  
Jayanti Yusmah Sari ◽  
Ika Putri Ningrum
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

Feature Extraction

2018 ◽  
pp. 59-66
Keyword(s):  
Feature Extraction ◽  
Dimensional Space ◽  
Large Datasets ◽  
High Dimensional ◽  
Basic Definition ◽  
High Dimensional Space ◽  
Existing Problems ◽  
Problems And Solutions ◽  
Definition Of ◽  
Selection Of

This chapter is an illustration of feature extraction for working with large datasets. The basic definition of feature extraction, selection of effective features, and the existing problems and solutions are provided. How feature extraction maps the high dimensional space to smaller space is explained.

scholarly journals Hybrid Approach for Face Recognition from a Single Sample per Person by Combining VLC and GOM

2019 ◽  
Vol 29 (1) ◽  
pp. 1523-1534 ◽  
Author(s):  
Ahmed Ghorbel ◽  
Walid Aydi ◽  
Imen Tajouri ◽  
Nouri Masmoudi
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Execution Time ◽  
Hybrid Approach ◽  
Recognition System ◽  
Single Sample ◽  
Data Sets ◽  
Extraction Techniques ◽  
Face Recognition System ◽  
Execution Speed

Abstract This paper proposes a new face recognition system based on combining two feature extraction techniques: the Vander Lugt correlator (VLC) and Gabor ordinal measures (GOM). The proposed system relies on the execution speed of VLC and the robustness of GOM. In this system, we applied the Tan and Triggs and retina modeling enhancement techniques, which are well suited for VLC and GOM, respectively. We evaluated our system on the standard FERET probe data sets and on extended YaleB database. The obtained results exhibited better face recognition rates in a shorter execution time compared to the GOM technique.

scholarly journals Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 38 ◽  
Author(s):  
Xiaohong Wu ◽  
Jin Zhu ◽  
Bin Wu ◽  
Chao Zhao ◽  
Jun Sun ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Electronic Nose ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Component Analysis ◽  
K Nearest Neighbor ◽  
Knn Classifier ◽  
Extraction Algorithm ◽  
Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

scholarly journals Multiple Data-Dependent Kernel Fisher Discriminant Analysis for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yue Liu ◽  
Yibing Li ◽  
Hong Xie ◽  
Dandan Liu
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Facial Features ◽  
Fisher Criterion ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Face Images ◽  
Kernel Fisher Discriminant Analysis ◽  
Multiple Data ◽  
Novel Algorithm

Kernel Fisher discriminant analysis (KFDA) method has demonstrated its success in extracting facial features for face recognition. Compared to linear techniques, it can better describe the complex and nonlinear variations of face images. However, a single kernel is not always suitable for the applications of face recognition which contain data from multiple, heterogeneous sources, such as face images under huge variations of pose, illumination, and facial expression. To improve the performance of KFDA in face recognition, a novel algorithm named multiple data-dependent kernel Fisher discriminant analysis (MDKFDA) is proposed in this paper. The constructed multiple data-dependent kernel (MDK) is a combination of several base kernels with a data-dependent kernel constraint on their weights. By solving the optimization equation based on Fisher criterion and maximizing the margin criterion, the parameter optimization of data-dependent kernel and multiple base kernels is achieved. Experimental results on the three face databases validate the effectiveness of the proposed algorithm.

An Improved Linear Discriminant Analysis Method and its Application to Face Recognition

2014 ◽  
Vol 556-562 ◽  
pp. 4825-4829 ◽  
Author(s):  
Kai Li ◽  
Peng Tang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Extraction Method ◽  
Analysis Method ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Discriminant Analysis Method

Linear discriminant analysis (LDA) is an important feature extraction method. This paper proposes an improved linear discriminant analysis method, which redefines the within-class scatter matrix and introduces the normalized parameter to control the bias and variance of eigenvalues. In addition, it makes the between-class scatter matrix to weight and avoids the overlapping of neighboring class samples. Some experiments for the improved algorithm presented by us are performed on the ORL, FERET and YALE face databases, and it is compared with other commonly used methods. Experimental results show that the proposed algorithm is the effective.

scholarly journals AN ADOPTION OF 2D-PCA/ICA BASED POST-PROCESSING DIMENSIONALITY REDUCTION ALGORITHM FOR FACIAL RECOGNITION SYSTEM

2020 ◽  
Vol 3 (2) ◽  
pp. 222-235
Author(s):  
Vivian Nwaocha ◽  
◽  
Ayodele Oloyede ◽  
Deborah Ogunlana ◽  
Michael Adegoke ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Nearest Neighbor ◽  
Gaussian Function ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Discrimination Ability ◽  
Face Images

Face images undergo considerable amount of variations in pose, facial expression and illumination condition. This large variation in facial appearances of the same individual makes most Existing Face Recognition Systems (E-FRS) lack strong discrimination ability and timely inefficient for face representation due to holistic feature extraction technique used. In this paper, a novel face recognition framework, which is an extension of the standard (PCA) and (ICA) denoted as two-dimensional Principal Component Analysis (2D-PCA) and two-dimensional Independent Component Analysis (2D-ICA) respectively is proposed. The choice of 2D was advantageous as image covariance matrix can be constructed directly using original image matrices. The face images used in this study were acquired from the publicly available ORL and AR Face database. The features belonging to similar class were grouped and correlation calculated in the same order. Each technique was decomposed into different components by employing multi-dimensional grouped empirical mode decomposition using Gaussian function. The nearest neighbor (NN) classifier is used for classification. The results of evaluation showed that the 2D-PCA method using ORL database produced RA of 92.5%, PCA produced RA of 75.00%, ICA produced RA of 77.5%, 2D-ICA produced RA of 96.00%. However, 2D-PCA methods using AR database produced RA of 73.56%, PCA produced RA of 62.41%, ICA produced RA of 66.20%, 2D-ICA method produced RA of 77.45%. This study revealed that the developed face recognition framework algorithm achieves an improvement of 18.5% and 11.25% for the ORL and AR databases respectively as against PCA and ICA feature extraction techniques. Keywords: computer vision, dimensionality reduction techniques, face recognition, pattern recognition

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