Facial Expression Detection Techniques: Based on Viola and Jones Algorithm and Principal Component Analysis

2015 ◽  
Author(s):  
Samiksha Agrawal ◽  
Pallavi Khatri
Keyword(s):  
Principal Component Analysis ◽  
Facial Expression ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Techniques

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.

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 Human Facial Emotion Recognition using Adaptive Sigmoidal Transfer Function in MLP Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 4103-4113
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Principal Component Analysis ◽  
Transfer Function ◽  
Facial Expression ◽  
Principal Component ◽  
Component Analysis ◽  
Activation Function ◽  
Facial Emotion ◽  
Self Adaptive

The human face is very sensitive towards inner feelings particularly with different state of mind under various conditions. The facial expression has used in computer vision to understand the human response against stimuli. But the facial expression is also having the nature of variability and controllability hence its complete generalization from a computer vision point of view is very difficult and challenging, though acceptable performances can be achieved. In this paper, a twostage based facial expression recognition model which carry the Principal component analysis as a feature extractor in the first stage and self-adaptive based activation function in feedforward neural network as a classifier in the second stage have applied. Use of principal component analysis reduces the dimension of features while the adaptive slope of transfer function provides another parameter along with weights to change in making learning faster and accurate. Six most dominant state of facial emotion like angry, surprise, sadness, normal, happy and fear have considered in this paper and performances have been tested over variable expressions. The benefit of the proposed model of self-adaptive activation function has verified through the benchmark XOR problem classification.

scholarly journals A Study of Feature Reduction Techniques and Classification for Network Anomaly Detection

2020 ◽  
Vol 27 (4) ◽  
pp. 1-16
Author(s):  
Meenal Jain ◽  
Gagandeep Kaur
Keyword(s):  
Principal Component Analysis ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Support Vector ◽  
Detection Techniques ◽  
Difference Measure ◽  
Reduction Techniques

Due to the launch of new applications the behavior of internet traffic is changing. Hackers are always looking for sophisticated tools to launch attacks and damage the services. Researchers have been working on intrusion detection techniques involving machine learning algorithms for supervised and unsupervised detection of these attacks. However, with newly found attacks these techniques need to be refined. Handling data with large number of attributes adds to the problem. Therefore, dimensionality based feature reduction of the data is required. In this work three reduction techniques, namely, Principal Component Analysis (PCA), Artificial Neural Network (ANN), and Nonlinear Principal Component Analysis (NLPCA) have been studied and analyzed. Secondly, performance of four classifiers, namely, Decision Tree (DT), Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Naïve Bayes (NB) has been studied for the actual and reduced datasets. In addition, novel performance measurement metrics, Classification Difference Measure (CDM), Specificity Difference Measure (SPDM), Sensitivity Difference Measure (SNDM), and F1 Difference Measure (F1DM) have been defined and used to compare the outcomes on actual and reduced datasets. Comparisons have been done using new Coburg Intrusion Detection Data Set (CIDDS-2017) dataset as well widely referred NSL-KDD dataset. Successful results were achieved for Decision Tree with 99.0 percent and 99.8 percent accuracy on CIDDS and NSLKDD datasets respectively.

scholarly journals Comparative Analysis of Change Detection Techniques In Landuse / Landcover Mapping of Oyo Town, Oyo State, Nigeria

2020 ◽  
pp. 44-62
Author(s):  
Olaniyi Saheed S. ◽  
Igbokwe J. I ◽  
Ojiako J. C.
Keyword(s):  
Principal Component Analysis ◽  
Comparative Analysis ◽  
Change Detection ◽  
Accuracy Assessment ◽  
Principal Component ◽  
Component Analysis ◽  
United States Geological Survey ◽  
Landsat Images ◽  
Detection Techniques ◽  
Analysis Of Change

Landcover is the natural surface of the earth undisturbed by human activities. It represents vegetation, natural or man-made features and every other visible evidence of land use. Landuse on the other hand refers to the use of land by humans while Change detection is the process of identifying differences in the state of an object or phenomenon by observing it in different times. This study is aimed at comparative analysis of change detection techniques in landuse/ landcover mapping of Oyo town with the objectives of comparing and evaluating the results of different change detection techniques as well as production of Landuse/ Landcover map of the study area for the period of 1990 and 2016. Landsat images of 1990, 2003 and 2016 covering the study area (Path 191, Row 54 & 55) were collected from the archives of United States Geological Survey (USGS) agency and image processing and analysis were done using ERDAS Imagine 2015 and ArcGIS 10.5. The results of the study were achieved through image pre-processing, image enhancement, image band combination, change detection through pre-classification (image differencing, image ratioing, Principal Component Analysis) and Post-Classification Comparison (PCC) methods, and results analysed. The result of accuracy assessment in this research shows that a PCA produces a better result of 91.67% while PCC delivered accuracy that ranges between 83.33% and 87.5%. However, PCC gives a better result on the change detection in the study area as it affords more analysis on the study area based on the thematic classes generated for each landuse and landcover of the study area. This study hereby recommends Post-Classification Comparison (PCC) and Principal Component Analysis (PCA) for change detection in the study area. Further research on change detection in the study area should be carried out using Object-Based Image Analysis (OBIA) using high resolution images because this research is hinge on pixel based classification of medium resolution images.

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