Performance analysis of different machine learning approaches for single modal facial expression detection

Cataloged from PDF version of thesis.

Bibliographic Details
Main Authors:	Zinia, Surovi, Azmol, Aliza Ibn, Islam, Saiful
Other Authors:	Alam, Dr. Md Ashraful
Format:	Thesis
Language:	English
Published:	BRAC University 2018
Subjects:	Artificial Neural Network (ANN) Logistic regression Principal component analysis Facial expression. Machine learning.
Online Access:	http://hdl.handle.net/10361/10843

id	10361-10843
record_format	dspace
spelling	10361-108432022-01-26T10:18:26Z Performance analysis of different machine learning approaches for single modal facial expression detection Zinia, Surovi Azmol, Aliza Ibn Islam, Saiful Alam, Dr. Md Ashraful Department of Computer Science and Engineering, BRAC University Artificial Neural Network (ANN) Logistic regression Principal component analysis Facial expression. Machine learning. Cataloged from PDF version of thesis. Includes bibliographical references (pages 45-48). This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Facial expression detection plays a pivotal role in the studies of emotion, cognitive processes, and social interaction. This has potential applications in different aspects of everyday life .For Example, real time face detection, sentiment analysis, CCTV violence prediction. In this thesis, we investigate and analyze the performance of different machine learning approaches for single modal type facial expression detection. With this proposed model, it is observed that the feature extraction techniques incorporated in this work performs better in recognizing disparate expressions than feeding unprocessed raw dataset to the networks. Moreover, this study used Japanese Female Facial Expression (JAFFE) to demonstrate the comparative performance of different classical classifiers and neural network-based approaches and how viable they are in the detection of facial expression from single modal information. Hence this kind of models increase the advancement of facial recognition for more future purposes .Therefore, the proposed model proves the feasibility of computer vision based facial expression recognition for practical applications like surveillance and Human Computer Interaction (HCI). In this system, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) have been used to solve the dimensionality reduction and visual representation of the feature components in a 2D feature space. For classification and recognition tasks we used different classification algorithms like K-nearest Neighbor (KNN), Support Vector Machines (SVM), Gaussian Naïve Bayes, Random Forest, Extra Tree, Ensemble machines and vanilla neural networks. To use the total dataset on this algorithm we used 80% training and 20% testing of the total dataset. Finally the best accuracy result was given by Artificial Neural Network (ANN) which was 90.63 % from the proposed model. Surovi Zinia Aliza Ibn Azmol Saiful Islam B. Computer Science and Engineering 2018-11-14T04:54:18Z 2018-11-14T04:54:18Z 2018 8/13/2018 Thesis ID 13201058 ID 13321063 ID 13121125 http://hdl.handle.net/10361/10843 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf BRAC University
institution	Brac University
collection	Institutional Repository
language	English
topic	Artificial Neural Network (ANN) Logistic regression Principal component analysis Facial expression. Machine learning.
spellingShingle	Artificial Neural Network (ANN) Logistic regression Principal component analysis Facial expression. Machine learning. Zinia, Surovi Azmol, Aliza Ibn Islam, Saiful Performance analysis of different machine learning approaches for single modal facial expression detection
description	Cataloged from PDF version of thesis.
author2	Alam, Dr. Md Ashraful
author_facet	Alam, Dr. Md Ashraful Zinia, Surovi Azmol, Aliza Ibn Islam, Saiful
format	Thesis
author	Zinia, Surovi Azmol, Aliza Ibn Islam, Saiful
author_sort	Zinia, Surovi
title	Performance analysis of different machine learning approaches for single modal facial expression detection
title_short	Performance analysis of different machine learning approaches for single modal facial expression detection
title_full	Performance analysis of different machine learning approaches for single modal facial expression detection
title_fullStr	Performance analysis of different machine learning approaches for single modal facial expression detection
title_full_unstemmed	Performance analysis of different machine learning approaches for single modal facial expression detection
title_sort	performance analysis of different machine learning approaches for single modal facial expression detection
publisher	BRAC University
publishDate	2018
url	http://hdl.handle.net/10361/10843
work_keys_str_mv	AT ziniasurovi performanceanalysisofdifferentmachinelearningapproachesforsinglemodalfacialexpressiondetection AT azmolalizaibn performanceanalysisofdifferentmachinelearningapproachesforsinglemodalfacialexpressiondetection AT islamsaiful performanceanalysisofdifferentmachinelearningapproachesforsinglemodalfacialexpressiondetection
_version_	1814308880554917888

Performance analysis of different machine learning approaches for single modal facial expression detection

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