Making faces with computers

2005 ◽  
Vol 13 (3) ◽  
pp. 459-479 ◽  
Author(s):  
Graham Pike ◽  
Nicola Brace ◽  
Jim Turner ◽  
Sally Kynan
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Construction Process ◽  
Appropriate Use ◽  
Two Generations ◽  
The Face ◽  
Preliminary Study

Knowledge concerning the cognition involved in perceiving and remembering faces has informed the design of at least two generations of facial compositing technology. These systems allow a witness to work with a computer (and a police operator) in order to construct an image of a perpetrator. Research conducted with systems currently in use has suggested that basing the construction process on the witness recalling and verbally describing the face can be problematic. To overcome these problems and make better use of witness cognition, the latest systems use a combination of Principal Component Analysis (PCA) facial synthesis and an array-based interface. The present paper describes a preliminary study conducted to determine whether the use of an array-based interface really does make appropriate use of witness cognition and what issues need to be considered in the design of emerging compositing technology.

scholarly journals Improved Approach for Identification of Real and Fake Smile using Chaos Theory and Principal Component Analysis

2019 ◽  
Vol 54 (5) ◽  
Author(s):  
Hayder Ansaf ◽  
Hayder Najm ◽  
Jasim Mohammed Atiyah ◽  
Oday A. Hassen
Keyword(s):  
Principal Component Analysis ◽  
Chaos Theory ◽  
Principal Component ◽  
Component Analysis ◽  
Human Beings ◽  
Multiple Parameters ◽  
Detection Approach ◽  
The Face ◽  
And Performance ◽  
Smile Detection

The smile detection approach is quite prominent with the face detection and thereby the enormous implementations are prevalent so that the higher degree of accuracy can be achieved. The face smile detection is widely associated to have the forensic of faces of human beings so that the future predictions can be done. In chaos theory, the main strategy is to have the cavernous analytics on the single change and then to predict the actual faces in the analysis. In addition, the integration of Principal Component Analysis (PCA) is integrated to have the predictions with more accuracy. This work proposes to use the analytics on the parallel integration of PCA and chaos theory to enable the face smile and fake identifications to be made possible. The projected work is analyzed using assorted parameters and it has been found that the deep learning integration approach for chaos and PCA is quite important and performance aware in the multiple parameters with the different datasets in evaluations.

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

scholarly journals PRINCIPAL COMPONENT ANALYSIS BIPLOT GLOBAL COMPETITIVENESS INDEX ASEAN COUNTRIES

2021 ◽  
Vol 14 (2) ◽  
pp. 93
Author(s):  
Lina Sari ◽  
Pardomuan Robinson Sihombing
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Economic Interests ◽  
Global Competitiveness ◽  
The Face ◽  
Global Competitiveness Index ◽  
Competitiveness Index ◽  
Asean Countries ◽  
State Enterprises

ASEAN's global competitiveness requires institutional and ASEAN countries appear to be a formidable economic actors in protecting the economic interests and at the same time having an open economic system that indicates the readiness of ASEAN to compete with the economic strength of the entire region in the world. In this case the measurement of global competitiveness factors become important aspects of state enterprises in the face of global competition. This study was conducted to determine how competitive the ASEAN countries with Biplot method of Principal Component Analysis. Results obtained from this study is the ASEAN countries have different advantages in each of the variables related to the global competitiveness index. In addition, the diversity of which can be explained more than 70% which is 90.69% which means that Principal Component Analysis Biplot describes well the overall total

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.

scholarly journals Diagnosis of Esophagitis Based on Face Recognition Techniques

2010 ◽  
Vol 4 (1) ◽  
pp. 58-62
Author(s):  
Santosh S Saraf ◽  
Gururaj R Udupi ◽  
Santosh D Hajare
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Techniques ◽  
The Face ◽  
Pca Method ◽  
Recognition Efficiency ◽  
Motivating Factor

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.

MATLAB Implementation of Face Identification Using Principal Component Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 5402-5408
Author(s):  
Nasrul Humaimi Mahmood ◽  
Ismail Ariffin ◽  
Camallil Omar ◽  
Nur Sufiah Jaafar
Keyword(s):  
Principal Component Analysis ◽  
Minimum Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Face Identification ◽  
Human Face ◽  
Face Images ◽  
The Face ◽  
Matlab Implementation ◽  
Pca Algorithm

Face is the greatest superior biometric as the face has a complex, multidimensional and meaningful identity compared from one person to another. Face identification is executed by comparing the characteristics of the face (test image) with those of known individual images in the database. This paper describes the used of the Principal Component Analysis (PCA) algorithm for human face identification based on webcam image. The MATLAB is used as a tool for image processing and analysis. The important decision to identify the person is by the minimum distance of the face images and known face images in face space. From the results, it can be concluded that the work has successfully implemented the PCA algorithm for human face identification through a webcam.

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