Face Recognition Using Incremental Principal Components Analysis

2009 ◽  
Author(s):  
Hatim A. Aboalsamh ◽  
Hassan I. Mathkour ◽  
Ghazy M.R. Assassa ◽  
Mona F.M. Mursi
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

scholarly journals Face recognition system based on principal components analysis and distance measures

2018 ◽  
Vol 7 (2.21) ◽  
pp. 15 ◽  
Author(s):  
T Meenpal ◽  
Aarti Goyal ◽  
Ankita Meenpal
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition System ◽  
Data Representation ◽  
Vital Role ◽  
Distance Measures ◽  
Face Recognition System ◽  
Components Analysis ◽  
Matching Techniques

Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.  

scholarly journals RANCANG BANGUN SISTEM PENGENALAN WAJAH DENGAN METODE PRINCIPAL COMPONENT ANALYSIS

2016 ◽  
Vol 1 (2) ◽  
pp. 59-75
Author(s):  
Salamun Salamun ◽  
Firman Wazir
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Physiological Measures ◽  
Test Results ◽  
Analysis Method ◽  
The Face ◽  
Components Analysis

The face is one of the easiest physiological measures and is often used to distinguish individual identities from one another. This facial recognition process uses raw information from pixel images generated through a camera which is then represented in the Principal Components Analysis method. The Principal Components Analysis method works by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector will get the value of each image eigenface and then the nearest eigenface value of the image will be found and then the nearest eigenface value of the image will be found the image of the face you want to recognize. The test results showed an overall success rate of face recognition of 82.27% with face data of 130 images.

Factored principal components analysis, with applications to face recognition

2008 ◽  
Vol 19 (3) ◽  
pp. 229-238 ◽  
Author(s):  
Ian L. Dryden ◽  
Li Bai ◽  
Christopher J. Brignell ◽  
Linlin Shen
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Components Analysis

scholarly journals A Proposed Generalized Eigenfaces System for Face Recognition Based on One Training Image

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Shaimaa Khudhair Salah ◽  
Waleed Rasheed Humood ◽  
Ahmed Othman Khalaf
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Training Image ◽  
Training Data ◽  
Gray Level ◽  
Analysis Algorithm ◽  
Front End ◽  
Biological Treatments ◽  
Components Analysis

This paper discusses the results of a study that aimed to develop an eigenface technique known as (PC) 2A that collect the image of the original face with its vertical and horizontal projections. The basic components of the image were analyzed in the image enrichment section. An evaluation of the proposed method demonstrates that it costs less than the standard eigenface technique. Moreover, the experimental results show that a front-end database that has a gray level for each person has one training image; thus, in terms of accuracy, it was possible to get a 3-5% result for the proposed (PC)2A, which is higher than the precision of the standard eigenface technique. The main objective of this paper is to demonstrate the weaknesses and strengthens of the facial recognition approach as an identifier known as eigenfaces. This aim was achieved by using the principal components analysis algorithm based on the images of previously stored training data. The outcomes show the strength of the proposed technique, in which it was possible to obtain accuracy results of up to 96%, which in turn provides support for developing the technique proposed in this paper in the future because this work is of great importance in the field of biological treatments, the need for which has significantly increased over the last 5 years.

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