scholarly journals PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Kanokmon Rujirakul ◽  
Chakchai So-In ◽  
Banchar Arnonkijpanich
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Expectation Maximization ◽  
High Speed ◽  
Expectation Maximization Algorithm ◽  
Parallel Architecture ◽  
Principal Component ◽  
Eigenvalue Decomposition ◽  
Computational Time ◽  
Recognition Systems

Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA.

scholarly journals Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Abdulbasit Alazzawi ◽  
Osman N. Ucan ◽  
Oguz Bayat
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Linear Regression ◽  
Edge Detection ◽  
Extraction Method ◽  
Principal Component ◽  
Recognition System ◽  
Detection Techniques ◽  
Face Recognition System ◽  
Recognition Systems

Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

scholarly journals Maximizing the Reliability of Cross-National Measures of Presidential Power

2014 ◽  
Vol 46 (4) ◽  
pp. 731-741 ◽  
Author(s):  
David Doyle ◽  
Robert Elgie
Keyword(s):  
Expectation Maximization ◽  
Expectation Maximization Algorithm ◽  
Principal Component ◽  
Likelihood Estimation ◽  
Standard Errors ◽  
Presidential Power ◽  
Single Measure ◽  
Future Studies ◽  
Time Periods ◽  
Cross National

This article aims to maximize the reliability of presidential power scores for a larger number of countries and time periods than currently exists for any single measure, and in a way that is replicable and easy to update. It begins by identifying all of the studies that have estimated the effect of a presidential power variable, clarifying what scholars have attempted to capture when they have operationalized the concept of presidential power. It then identifies all the measures of presidential power that have been proposed over the years, noting the problems associated with each. To generate the new set of presidential power scores, the study draws upon the comparative and local knowledge embedded in existing measures of presidential power. Employing principal component analysis, together with the expectation maximization algorithm and maximum likelihood estimation, a set of presidential power scores is generated for a larger set of countries and country time periods than currently exists, reporting 95 per cent confidence intervals and standard errors for the scores. Finally, the implications of the new set of scores for future studies of presidential power is discussed.

Matrix Based Feature Measurement and Extraction for Face Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 643-647
Author(s):  
Qi Zhu ◽  
Jin Rong Cui ◽  
Zi Zhu Fan
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Measurement Method ◽  
Principal Component ◽  
Component Analysis ◽  
High Accuracy ◽  
Face Image ◽  
Two Dimensional ◽  
The Matrix

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

Face Recognition Using KPCA and KFDA

2013 ◽  
Vol 380-384 ◽  
pp. 3850-3853 ◽  
Author(s):  
Hong Mei Li ◽  
Dong Ming Zhou ◽  
Ren Can Nie ◽  
Xiang Li ◽  
Hai Ying Deng
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Principal Component ◽  
Nonlinear Method

KPCA extracting principal component with nonlinear method is an improved PCA. The KPCA can extract the feature set which is more suitable in categorization than the conventional PCA. The method of KFDA is equivalent to KPCA plus LDA. KPCA is first performed and then LDA is used for a second feature extraction in the KPCA-transformed space. The KPCA and KFDA have been got widely used in feature extraction and face recognition. In this paper, the method of KPCA and KFDA is analyzed and their nature is revealed. Finally, the effectiveness of the algorithm is verified using the ORL database.

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

Pose and Illumination Invariant Hybrid Feature Extraction for Newborn

2019 ◽  
Vol 12 ◽  
Author(s):  
Rishav Singh ◽  
Ritika Singh ◽  
Aakriti Acharya ◽  
Shrikant Tiwari ◽  
Hari Om
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Hybrid Approach ◽  
Controlled Environment ◽  
Illumination Invariant ◽  
Speeded Up Robust Features ◽  
Hybrid Feature Extraction ◽  
Recognition Systems ◽  
Gallery Image

Recently a lot of face recognition systems are being designed to identify individuals in a semi controlled environment where pose and illumination are controlled. However, in the case of newborns it is not easy to click the photographs with similar pose and illumination. Here, in this paper a hybrid approach using Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP) is proposed for newborns. Moreover, in this paper the experiment is done for a single gallery image with improved results. It shows that the proposed method has 97.18% accuracy which is an 8% improvement over LBP and 8.6% improvement over SURF for Rank 5.

scholarly journals Multiple scaled contaminated normal distribution and its application in clustering

2019 ◽  
pp. 1471082X1989093 ◽  
Author(s):  
Antonio Punzo ◽  
Cristina Tortora
Keyword(s):  
Expectation Maximization ◽  
Heavy Tails ◽  
Expectation Maximization Algorithm ◽  
Simulated Data ◽  
Principal Component ◽  
Multivariate Normal ◽  
Degree Of Contamination ◽  
Robust Estimates ◽  
Directional Detection ◽  
Heavy Tailed

The multivariate contaminated normal (MCN) distribution represents a simple heavy-tailed generalization of the multivariate normal (MN) distribution to model elliptical contoured scatters in the presence of mild outliers (also referred to as ‘bad’ points herein) and automatically detect bad points. The price of these advantages is two additional parameters: proportion of good observations and degree of contamination. However, in a multivariate setting, only one proportion of good observations and only one degree of contamination may be limiting. To overcome this limitation, we propose a multiple scaled contaminated normal (MSCN) distribution. Among its parameters, we have an orthogonal matrix Γ. In the space spanned by the vectors (principal components) of Γ, there is a proportion of good observations and a degree of contamination for each component. Moreover, each observation has a posterior probability of being good with respect to each principal component. Thanks to this probability, the method provides directional robust estimates of the parameters of the nested MN and automatic directional detection of bad points. The term ‘directional’ is added to specify that the method works separately for each principal component. Mixtures of MSCN distributions are also proposed, and an expectation-maximization algorithm is used for parameter estimation. Real and simulated data are considered to show the usefulness of our mixture with respect to well-established mixtures of symmetric distributions with heavy tails.

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