scholarly journals IMPROVED REAL-TIME FACE RECOGNITION BASED ON THREE LEVEL WAVELET DECOMPOSITION-PRINCIPAL COMPONENT ANALYSIS AND MAHALANOBIS DISTANCE

2014 ◽  
Vol 10 (5) ◽  
pp. 844-851 ◽  
Author(s):  
Winarno
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Real Time ◽  
Mahalanobis Distance ◽  
Wavelet Decomposition ◽  
Principal Component ◽  
Component Analysis

scholarly journals Sistem Kontrol Akses Berbasis Real Time Face Recognition dan Gender Information

2015 ◽  
Vol 6 (2) ◽  
pp. 198
Author(s):  
Putri Nurmala ◽  
Wikaria Gazali ◽  
Widodo Budiharto
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Real Time ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Identification System ◽  
Face Recognition System ◽  
Gender Information ◽  
And Gender

Face recognition and gender information is a computer application for automatically identifying or verifying a person's face from a camera to capture a person's face. It is usually used in access control systemsand it can be compared to other biometrics such as finger print identification system or iris. Many of face recognition algorithms have been developed in recent years. Face recognition system and gender information inthis system based on the Principal Component Analysis method (PCA). Computational method has a simple and fast compared with the use of the method requires a lot of learning, such as artificial neural network. In thisaccess control system, relay used and Arduino controller. In this essay focuses on face recognition and gender - based information in real time using the method of Principal Component Analysis ( PCA ). The result achievedfrom the application design is the identification of a person’s face with gender using PCA. The results achieved by the application is face recognition system using PCA can obtain good results the 85 % success rate in face recognition with face images that have been tested by a few people and a fairly high degree of accuracy.

Wavelet decomposition based principal component analysis for face recognition using MATLAB

2016 ◽  
Author(s):  
Mahesh Kumar Sharma ◽  
Shashikant Sharma ◽  
Nopbhorn Leeprechanon ◽  
Aashish Ranjan
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Wavelet Decomposition ◽  
Principal Component ◽  
Component Analysis

scholarly journals Real-Time Principal Component Analysis

2020 ◽  
Vol 1 (2) ◽  
pp. 1-36
Author(s):  
Ranak Roy Chowdhury ◽  
Muhammad Abdullah Adnan ◽  
Rajesh K. Gupta
Keyword(s):  
Principal Component Analysis ◽  
Real Time ◽  
Principal Component ◽  
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.

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