AdaBoost based Random forest model for Emotion classification of Facial images
Posting of visual data in the social network has now become a common trend. Mainly, users are posting selfies or facial images over the social media that depict various moods at different instances. This has attracted the attention of researchers to come up with facial expression mining from social...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123004181 |
| _version_ | 1797405476993892352 |
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| author | Kumari Gubbala M. Naveen Kumar A. Mary Sowjanya |
| author_facet | Kumari Gubbala M. Naveen Kumar A. Mary Sowjanya |
| author_sort | Kumari Gubbala |
| collection | DOAJ |
| description | Posting of visual data in the social network has now become a common trend. Mainly, users are posting selfies or facial images over the social media that depict various moods at different instances. This has attracted the attention of researchers to come up with facial expression mining from social media images. Aim of the present work is to improve the performance of emotion analysis in a more efficient way in terms of accuracy and reliability. Developing new strategies for carrying out emotion analysis on posts containing images in social media. In this work, a novel model has been presented that focuses on transformed features for the purpose. Six distinct sentimental emotion classes (labeled 0 through 5) are considered in this work. They are 0: Sad, 1: Fear, 2: Awful, 3: Happy, 4: Surprised, 5: Satisfied. This model consists of three major stages: Feature extraction, Feature selection, and Class labeling. • This work incorporates the use of 2D Ortho-normal Stockwell Transformation (DOST) method is used for feature extraction of facial images. • Following the feature extraction model, feature selection is implemented through ‘bi-variate t-test’. • Finally, these selected features are subjected to a AdaBoost based Random Forest classifier for Emotion Classification(ARFEC) for the purpose of class labeling towards different classes of expression. The Flickr8k, CK+ and FER2013 image databases are utilized for validating the efficiency of the developed ARFEC model. Analysis of results shows the effectiveness of ARFEC model with overall rates of accuracy of 89.5 %, 92.5 % and 89.5 % respectively for the databases taken. Performance of ARFEC model when compared with other existing methods such as Support Vector Machine and K-Nearest Neighbors yielded better results in terms of overall rate of accuracy. |
| first_indexed | 2024-03-09T03:10:31Z |
| format | Article |
| id | doaj.art-f59a3b78db354d7393f68176384057a2 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-09T03:10:31Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-f59a3b78db354d7393f68176384057a22023-12-04T05:22:40ZengElsevierMethodsX2215-01612023-12-0111102422AdaBoost based Random forest model for Emotion classification of Facial imagesKumari Gubbala0M. Naveen Kumar1A. Mary Sowjanya2Department of CSE, CMR Engineering College, Hyderabad, Telangana, India; Corresponding author.Department of CSE, CMR Engineering College, Hyderabad, Telangana, IndiaDepartment of CS&SE, Andhra University College of Engineering (A), Visakhapatnam, Andhra Pradesh 530003, IndiaPosting of visual data in the social network has now become a common trend. Mainly, users are posting selfies or facial images over the social media that depict various moods at different instances. This has attracted the attention of researchers to come up with facial expression mining from social media images. Aim of the present work is to improve the performance of emotion analysis in a more efficient way in terms of accuracy and reliability. Developing new strategies for carrying out emotion analysis on posts containing images in social media. In this work, a novel model has been presented that focuses on transformed features for the purpose. Six distinct sentimental emotion classes (labeled 0 through 5) are considered in this work. They are 0: Sad, 1: Fear, 2: Awful, 3: Happy, 4: Surprised, 5: Satisfied. This model consists of three major stages: Feature extraction, Feature selection, and Class labeling. • This work incorporates the use of 2D Ortho-normal Stockwell Transformation (DOST) method is used for feature extraction of facial images. • Following the feature extraction model, feature selection is implemented through ‘bi-variate t-test’. • Finally, these selected features are subjected to a AdaBoost based Random Forest classifier for Emotion Classification(ARFEC) for the purpose of class labeling towards different classes of expression. The Flickr8k, CK+ and FER2013 image databases are utilized for validating the efficiency of the developed ARFEC model. Analysis of results shows the effectiveness of ARFEC model with overall rates of accuracy of 89.5 %, 92.5 % and 89.5 % respectively for the databases taken. Performance of ARFEC model when compared with other existing methods such as Support Vector Machine and K-Nearest Neighbors yielded better results in terms of overall rate of accuracy.http://www.sciencedirect.com/science/article/pii/S2215016123004181Sentiment analysisEmotion classificationFacial emotion2-dimensional discrete ortho-normal stock well transformation (DOST)AdaBoostRandom forest |
| spellingShingle | Kumari Gubbala M. Naveen Kumar A. Mary Sowjanya AdaBoost based Random forest model for Emotion classification of Facial images MethodsX Sentiment analysis Emotion classification Facial emotion 2-dimensional discrete ortho-normal stock well transformation (DOST) AdaBoost Random forest |
| title | AdaBoost based Random forest model for Emotion classification of Facial images |
| title_full | AdaBoost based Random forest model for Emotion classification of Facial images |
| title_fullStr | AdaBoost based Random forest model for Emotion classification of Facial images |
| title_full_unstemmed | AdaBoost based Random forest model for Emotion classification of Facial images |
| title_short | AdaBoost based Random forest model for Emotion classification of Facial images |
| title_sort | adaboost based random forest model for emotion classification of facial images |
| topic | Sentiment analysis Emotion classification Facial emotion 2-dimensional discrete ortho-normal stock well transformation (DOST) AdaBoost Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123004181 |
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