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 PROSES EKSTRAKSI DAN KLASIFIKASI CITRA EMOSI MENGGUNAKAN METODE PCA DAN CNN

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 484
Author(s):  
Resty Wulanningrum ◽  
Anggi Nur Fadzila ◽  
Danar Putra Pamungkas
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition

Manusia secara alami menggunakan ekspresi wajah untuk berkomunikasi dan menunjukan emosi mereka dalam berinteraksi sosial. Ekspresi wajah termasuk kedalam komunikasi non-verbal yang dapat menyampaikan keadaan emosi seseorang kepada orang yang telah mengamatinya. Penelitian ini menggunakan metode Principal Component Analysis (PCA) untuk proses ekstraksi ciri pada citra ekspresi dan metode Convolutional Neural Network (CNN) sebagai prosesi klasifikasi emosi, dengan menggunakan data Facial Expression Recognition-2013 (FER-2013) dilakukan proses training dan testing untuk menghasilkan nilai akurasi dan pengenalan emosi wajah. Hasil pengujian akhir mendapatkan nilai akurasi pada metode PCA sebesar 59,375% dan nilai akurasi pada pengujian metode CNN sebesar 59,386%.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

scholarly journals Differentially Expressed Genes Extracted by the Tensor Robust Principal Component Analysis (TRPCA) Method

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yue Hu ◽  
Jin-Xing Liu ◽  
Ying-Lian Gao ◽  
Sheng-Jun Li ◽  
Juan Wang
Keyword(s):  
Principal Component Analysis ◽  
Differentially Expressed Genes ◽  
Principal Component ◽  
Component Analysis ◽  
Differentially Expressed ◽  
Low Rank ◽  
Cancer Gene ◽  
Sequencing Data ◽  
Robust Principal Component Analysis ◽  
The Matrix

In the big data era, sequencing technology has produced a large number of biological sequencing data. Different views of the cancer genome data provide sufficient complementary information to explore genetic activity. The identification of differentially expressed genes from multiview cancer gene data is of great importance in cancer diagnosis and treatment. In this paper, we propose a novel method for identifying differentially expressed genes based on tensor robust principal component analysis (TRPCA), which extends the matrix method to the processing of multiway data. To identify differentially expressed genes, the plan is carried out as follows. First, multiview data containing cancer gene expression data from different sources are prepared. Second, the original tensor is decomposed into a sum of a low-rank tensor and a sparse tensor using TRPCA. Third, the differentially expressed genes are considered to be sparse perturbed signals and then identified based on the sparse tensor. Fourth, the differentially expressed genes are evaluated using Gene Ontology and Gene Cards tools. The validity of the TRPCA method was tested using two sets of multiview data. The experimental results showed that our method is superior to the representative methods in efficiency and accuracy aspects.

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.

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.

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