Facial Expressions Recognition Using Markov Stationary Feature - Vector Quantization and Support Vector Machine Method

Facial expression is a form of nonverbal communication that can convey the emotional state of someone to the person who observes it. Research on the recognition of facial expressions is one of the interesting fields in computer science. This research aims to improve the accuracy of recognition perfo...

Full description

Saved in:
Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2019-11, Vol.662 (2), p.22023
Main Authors: Maliki, I, Jarockohir, F S
Format: Article
Language:English
Subjects:
Feature extraction
Image classification
Support vector machines
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Facial expression is a form of nonverbal communication that can convey the emotional state of someone to the person who observes it. Research on the recognition of facial expressions is one of the interesting fields in computer science. This research aims to improve the accuracy of recognition performance. The process carried out in this research is to perform feature extraction and image classification. Markov Stationary Feature - Vector Quantization (MSF-VQ) method is used for feature extraction and Support Vector Machine (SVM) for image classification. Data set in used is 1440 data with six classifications of facial expressions. The results of the testing showed 97.41% which stated that this method could be recommended to be applied in the facial expressions recognition.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/662/2/022023