Emotion Analysis / Manilitphone Thephavanh
Material type:
TextPublication details: Bandar Seri Begawan : Universiti Teknologi Brunei , ©2019 Description: ix, 53 pages : color illustrations ; 30 cmSubject(s): -- Report Universiti Teknologi Brunei | Emotions -- Research | Emotions -- Psychological aspectsOther classification: UTB 120 REPORT THESIS & DISSERTATION | RTDS 272
| Item type | Current library | Call number | Copy number | Status | Notes | Date due | Barcode |
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Reports, Thesis & Dissertation Students
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Universiti Teknologi Brunei Library - at level 2 | UTB 120 REPORT THESIS & DISSERTATION, RTDS 272 (Browse shelf(Opens below)) | c.1 | Not for loan | Reg. No. 002029_UTB [RTDS 272] | 850448 | |
Report. Thesis & Desertation Students - Media
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Universiti Teknologi Brunei Library - at level 2 | UTB 120 REPORT THESIS & DISSERTATION, RTDS CD 23 (Browse shelf(Opens below)) | c.1 | Available | RTDS CD 23_UTB | 850449 |
Report submitted for the degree of BCs in Creative Multimedia Universiti Teknologi Brunei
Abstract
Emotion analysis is useful for many applications dealing with human emotions. A lot of time consuming in order to find the best practice and algorithm for emotion classification, detection and prediction because the analysis can be done in several techniques such as using Ekman's Facial Action Coding System (FACS), Geometric Feature, MPEG 4, facial landmarks detection by Dlib, etc. However, it's hard to achieve high accurate result with certain database and learning tools. This analysis purpose to explore and experiment various of machine learning techniques that can outcome with accuracy results for each technique in a certain condition. By using the Extended Cohn-Kanade Dataset (CK+) with appropriate data mining tool will help to analyze universal of human emotions and result with reasonable accuracy. This analysis focusing on facial landmarks features in order to classify different types of emotion.
Includes bibliographical references
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