Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian

Customer satisfaction measurement is one of the most crucial ways to identify and improve the business strategy of the organization and the department that involved is in the customer service management. One of the methods to measure the customer satisfaction is by trying to measure the customer emo...

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Main Author:	Saffian, Norhafizah
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Geometry Trigonometry Topology Instruments and machines Algorithms Database management
Online Access:	https://ir.uitm.edu.my/id/eprint/69044/1/69044.pdf
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id	my-uitm-ir.69044
record_format	uketd_dc
spelling	my-uitm-ir.690442022-10-23T14:26:22Z Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian 2017-01 Saffian, Norhafizah Geometry. Trigonometry. Topology Geometry. Shapes. General works, treatises, and textbooks Instruments and machines Electronic Computers. Computer Science Expert systems (Computer science). Fuzzy expert systems Algorithms Database management Customer satisfaction measurement is one of the most crucial ways to identify and improve the business strategy of the organization and the department that involved is in the customer service management. One of the methods to measure the customer satisfaction is by trying to measure the customer emotional aspect. Therefore, it is important to identify customer expression about the services provided and this can be solved using the facial expression recognition to get an accurate measurement of the customer satisfaction. The facial expression consists of three steps that are face detection, facial feature extraction, and classification of feature extraction. The main problem that occurs in measurement the customer satisfaction is the problems with the survey content and the way of the customer respond with the services that lead to an inaccurate customer satisfaction measurement. In this proposed project, the classification step is being focused on and become the main objective. The k-Nearest Neighbor classifier is applied as the classification algorithm. The confusion matrix calculation is used to measure the accuracy of k-NN classifier. Based on this calculation, the accuracy of this algorithm is 93% using the k values of 5. The future work that continue based on this project proposed is by study and applied other type of algorithm that can produce the high performance and accuracy of classification of facial feature for facial expression recognition. 2017-01 Thesis https://ir.uitm.edu.my/id/eprint/69044/ https://ir.uitm.edu.my/id/eprint/69044/1/69044.pdf text en public degree Universiti Teknologi MARA, Terengganu Faculty of Computer and Mathematical Sciences Raju, Rajeswari
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Raju, Rajeswari
topic	Geometry Trigonometry Topology Geometry Trigonometry Topology Instruments and machines Geometry Trigonometry Topology Geometry Trigonometry Topology Algorithms Database management
spellingShingle	Geometry Trigonometry Topology Geometry Trigonometry Topology Instruments and machines Geometry Trigonometry Topology Geometry Trigonometry Topology Algorithms Database management Saffian, Norhafizah Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
description	Customer satisfaction measurement is one of the most crucial ways to identify and improve the business strategy of the organization and the department that involved is in the customer service management. One of the methods to measure the customer satisfaction is by trying to measure the customer emotional aspect. Therefore, it is important to identify customer expression about the services provided and this can be solved using the facial expression recognition to get an accurate measurement of the customer satisfaction. The facial expression consists of three steps that are face detection, facial feature extraction, and classification of feature extraction. The main problem that occurs in measurement the customer satisfaction is the problems with the survey content and the way of the customer respond with the services that lead to an inaccurate customer satisfaction measurement. In this proposed project, the classification step is being focused on and become the main objective. The k-Nearest Neighbor classifier is applied as the classification algorithm. The confusion matrix calculation is used to measure the accuracy of k-NN classifier. Based on this calculation, the accuracy of this algorithm is 93% using the k values of 5. The future work that continue based on this project proposed is by study and applied other type of algorithm that can produce the high performance and accuracy of classification of facial feature for facial expression recognition.
format	Thesis
qualification_level	Bachelor degree
author	Saffian, Norhafizah
author_facet	Saffian, Norhafizah
author_sort	Saffian, Norhafizah
title	Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
title_short	Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
title_full	Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
title_fullStr	Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
title_full_unstemmed	Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian
title_sort	facial expression type recognition using k-nearest neighbor algorithm / norhafizah saffian
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2017
url	https://ir.uitm.edu.my/id/eprint/69044/1/69044.pdf
_version_	1783735836058058752

Facial expression type recognition using K-Nearest Neighbor algorithm / Norhafizah Saffian

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