scholarly journals Comparative Analysis of Illumination Normalizations on Principal Component Analysis Based Feature Extraction for Face Recognition

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Oluwakemi C Abikoye ◽  
Iyabo F Shoyemi ◽  
Taye O Aro
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Discrete Cosine Transform ◽  
Principal Component ◽  
Histogram Equalization ◽  
Component Analysis ◽  
Recognition System ◽  
Adaptive Histogram Equalization ◽  
Face Images

Principle Component Analysis (PCA) is an appearance-based technique for extraction of feature extraction that is commonly used in computer vision and image processing. This technique suffers from illumination variations, thus knowing which illumination control method to be used in PCA-based face recognition system is very important. This paper applies three selected normalization techniques; Discrete Cosine Transform (DCT), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to normalize face images. PCA was further used to extract features from the normalized face images. Euclidean distance was used to classify extracted features. The best recognition accuracy of 91.84% was obtained in DCT for ORL Database, while the best accuracy of 76% was achieved in DCT for FERET Database. The highest FAR of 0.9 was achieved in DCT for ORL Database, while the highest FAR of 0.5 was obtained in DCT and AHE for FERET Database. The highest FRR of 0.2821 was achieved in CLAHE for ORL Database, while 0.3000 was obtained in AHE for FERET Database. It was concluded that illumination control approaches have predominant effect on PCA–based facial recognition system. Keywords— Adaptive Histogram Equalization, Contrast Adaptive Histogram Equalization, Discrete Cosine Transform Illumination Normalization, Principal Component Analysis

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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.

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

scholarly journals Half-face based recognition using principal component analysis

2021 ◽  
Vol 22 (3) ◽  
pp. 1404
Author(s):  
Ahmed M. Alkababji ◽  
Sara Raed Abd
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Method ◽  
Face Images ◽  
The Face ◽  
Considerable Problem ◽  
Full Face ◽  
Pca Algorithm

<span lang="EN-US">Face recognition is a considerable problem in the field of image processing. It is used daily in various applications from personal cameras to forensic investigations. Most of the provides solutions proposed based on full-face images, are slow to compute and need more storage. In this paper, we propose an effective way to reduce the features and size of the database in the face recognition method and thus we get an increase in the speed of discrimination by using half of the face. Taking advantage of face symmetry, the first step is to divide the face image into two halves, then the left half is processed using the principal component analysis (PCA) algorithm, and the results are compared by using Euclidian distance to distinguish the person. The system was trained and tested on ORL database. It was found that the accuracy of the system reached up to 96%, and the database was minimized by 46% and the running time was decreased from 120 msec to 70 msec with a 41.6% reduction.</span>

scholarly journals Face Recognition Using Eigen Face Based Technique Utilizing the Concept of Principal Component Analysis

2013 ◽  
Vol 10 (8) ◽  
pp. 1943-1953
Author(s):  
Prof. Samir K Bandyopadhyay ◽  
Mrs. Sunita Roy
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Computation Time ◽  
Principal Component ◽  
Component Analysis ◽  
Research Area ◽  
Real Time System ◽  
Face Images ◽  
Small Set ◽  
Active Research

Face recognition has been an active research area since late 1980s [1]. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland [1] in 1991. In this approach we have to perform a lots of computations, which are not feasible with respect to time in many real time system. The concept of principal component analysis (PCA) is used in this approach to reduce the dimension and hence reducing the computation time. Principal component analysis [4] decomposes face images into a small set of characteristic feature images called eigen faces.

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