Facial expression using Histogram of Oriented Gradients and Ensemble Classifier

In this research, two methods were proposed to design a new system to recognize facial expressions. The first method relies on extracting features from the face area, and the second method relies on the process of extracting features on the parts of the face (eyes, nose, and mouth) where the histog...

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Main Authors: Maher Kh. Hussien, Fawziya Mahmood Ramo
Format: Article
Language:English
Published: Tikrit University 2022-11-01
Series:Tikrit Journal of Pure Science
Subjects:
Online Access:https://tjpsj.org/index.php/tjps/article/view/67
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author Maher Kh. Hussien
Fawziya Mahmood Ramo
author_facet Maher Kh. Hussien
Fawziya Mahmood Ramo
author_sort Maher Kh. Hussien
collection DOAJ
description In this research, two methods were proposed to design a new system to recognize facial expressions. The first method relies on extracting features from the face area, and the second method relies on the process of extracting features on the parts of the face (eyes, nose, and mouth) where the histogram of oriented gradients (HOG) algorithm was used in the feature extraction process in addition to the principal component analysis algorithm to reduce feature dimensions in both methods. We have proposed a group classifier consisting of three basic classifiers: support vector machines, knn-algorithm closest to neighbors, and Naive Bayes in the classification stage. Our proposed algorithm was tested on japanese female facial expression (JAFFE) Dataset and Cohn-Kanade (CK) dataset. It was found that higher overall accuracy is achieved for F1-Score when using the second method of 93.82 % and 94.12% for CK and JAFFA, respectively.
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publishDate 2022-11-01
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spelling doaj-art-d467ac1d4e004efdb71a17e488bf231a2025-08-20T02:36:41ZengTikrit UniversityTikrit Journal of Pure Science1813-16622415-17262022-11-0127210.25130/tjps.v27i2.67Facial expression using Histogram of Oriented Gradients and Ensemble ClassifierMaher Kh. HussienFawziya Mahmood Ramo In this research, two methods were proposed to design a new system to recognize facial expressions. The first method relies on extracting features from the face area, and the second method relies on the process of extracting features on the parts of the face (eyes, nose, and mouth) where the histogram of oriented gradients (HOG) algorithm was used in the feature extraction process in addition to the principal component analysis algorithm to reduce feature dimensions in both methods. We have proposed a group classifier consisting of three basic classifiers: support vector machines, knn-algorithm closest to neighbors, and Naive Bayes in the classification stage. Our proposed algorithm was tested on japanese female facial expression (JAFFE) Dataset and Cohn-Kanade (CK) dataset. It was found that higher overall accuracy is achieved for F1-Score when using the second method of 93.82 % and 94.12% for CK and JAFFA, respectively. https://tjpsj.org/index.php/tjps/article/view/67Facial ExpressionHOGJAFFAan ensemble classifier
spellingShingle Maher Kh. Hussien
Fawziya Mahmood Ramo
Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
Tikrit Journal of Pure Science
Facial Expression
HOG
JAFFA
an ensemble classifier
title Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
title_full Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
title_fullStr Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
title_full_unstemmed Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
title_short Facial expression using Histogram of Oriented Gradients and Ensemble Classifier
title_sort facial expression using histogram of oriented gradients and ensemble classifier
topic Facial Expression
HOG
JAFFA
an ensemble classifier
url https://tjpsj.org/index.php/tjps/article/view/67
work_keys_str_mv AT maherkhhussien facialexpressionusinghistogramoforientedgradientsandensembleclassifier
AT fawziyamahmoodramo facialexpressionusinghistogramoforientedgradientsandensembleclassifier