Facial Expression Recognition By Using Fisherface Methode With Backpropagation Neural Network
Zaenal Abidin(1*), Agus Harjoko(2)
(1) 
(2) 
(*) Corresponding Author
Abstract
Abstract— In daily lives, especially in interpersonal communication, face often used for expression. Facial expressions give information about the emotional state of the person. A facial expression is one of the behavioral characteristics. The components of a basic facial expression analysis system are face detection, face data extraction, and facial expression recognition. Fisherface method with backpropagation artificial neural network approach can be used for facial expression recognition. This method consists of two-stage process, namely PCA and LDA. PCA is used to reduce the dimension, while the LDA is used for features extraction of facial expressions. The system was tested with 2 databases namely JAFFE database and MUG database. The system correctly classified the expression with accuracy of 86.85%, and false positive 25 for image type I of JAFFE, for image type II of JAFFE 89.20% and false positive 15, for type III of JAFFE 87.79%, and false positive for 16. The image of MUG are 98.09%, and false positive 5.
Full Text:
PDFDOI: https://doi.org/10.22146/ijccs.2010
Article Metrics
Abstract views : 2307 | views : 2860Refbacks
- There are currently no refbacks.
Copyright (c) 2011 IJCCS - Indonesian Journal of Computing and Cybernetics Systems
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1