scholarly journals Generalized N-Dimensional Principal Component Analysis (GND-PCA) Based Statistical Appearance Modeling of Facial Images with Multiple Modes

2009 ◽  
Vol 1 ◽  
pp. 231-241 ◽  
Author(s):  
Xu Qiao ◽  
Rui Xu ◽  
Yen-Wei Chen ◽  
Takanori Igarashi ◽  
Keisuke Nakao ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Appearance Modeling ◽  
Multiple Modes ◽  
Facial Images

Smiling Reduces Masculinity: Principal Component Analysis Applied to Facial Images

Perception ◽  
10.1068/p5811 ◽  
2008 ◽  
Vol 37 (11) ◽  
pp. 1637-1648 ◽  
Author(s):  
Satoru Kawamura ◽  
Masashi Komori ◽  
Yusuke Miyamoto
Keyword(s):  
Principal Component Analysis ◽  
Facial Expression ◽  
Computational Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Physical Relationship ◽  
The Matrix ◽  
The Face ◽  
And Gender ◽  
Facial Images

We examined the effect of facial expression on the assignment of gender to facial images. A computational analysis of the facial images was applied to examine whether physical aspects of the face itself induced this effect. Thirty-six observers rated the degree of masculinity of the faces of 48 men, and the degree of femininity of the faces of 48 women. Half of the faces had a neutral facial expression, and the other half was smiling. Smiling significantly reduced the perceived masculinity of men's faces, especially for male observers, whereas no effect of smiling on femininity ratings was obtained for women's faces. A principal component analysis was conducted on the matrix of pixel luminance values for each facial image × all the images. The third principle component explained a relatively high proportion of the variance of both facial expressions and gender of face. These results suggest that the effect of smiling on the assignment of gender is caused, at least in part, by the physical relationship between facial expression and face gender.

Principal Component Analysis for the Whole Facial Image With Pigmentation Separation and Application to the Prediction of Facial Images at Various Ages

2014 ◽  
Vol 58 (2) ◽  
pp. 205031-2050311 ◽  
Author(s):  
Saori Toyota ◽  
Izumi Fujiwara ◽  
Misa Hirose ◽  
Nobutoshi Ojima ◽  
Keiko Ogawa-Ochiai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Facial Image ◽  
Facial Images

scholarly journals Algorithm of Face Recognition by Principal Component Analysis

1970 ◽  
Vol 3 (2) ◽  
Author(s):  
Khalid A. S. Al-Khateeb and Jaiz A. Y. Johari
Keyword(s):  
Computer Vision ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Data Base ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
Computer Vision Applications ◽  
Simplified Algorithm ◽  
Facial Images

A face recognition algorithm based on Principal Component Analysis (PCA) has been developed and tested for computer vision applications. A database of about 400 facial images was used to test the algorithm. Each image is represented by a matrix (112 x 92), The data base is divided into subsets, where each subset represents one of 10 different individuals. A 96% rate of successful detection and a 90% rate of successful recognition were obtained. Several factors had to be standardized to provide a constrained environment in order to reduce error. The analysis is based on a set of eigenvectors that defines an Eigen Face (EF). The method proved to be simple and effective. The simplified algorithm and techniques expedited the process without seriously compromising the accuracy.

Basis generation and description of facial images using principal-component analysis

1997 ◽  
Vol 28 (10) ◽  
pp. 77-83
Author(s):  
Akinori Nagata ◽  
Toru Okazaki ◽  
Chang Seok Choi ◽  
Hiroshi Harashima
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Facial Images
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