Facial Expression recognition using Fuzzy Inference
System
Maedeh Rasoulzadeh
Introduction
This paper proposes a
novel fuzzy method for facial expression recognition on still images of the
face.
Approach
The new technique involves in extracting mathematical data
from some special regions of the face and fed them to a fuzzy rule-based
system. Fuzzification operation uses triangular membership functions for both
input and output.
Method
- Input image is preprocessed by wiener filter for smoothing and more distinction between face and background.
- 5 basic regions were extracted on face area of the preprocessed image by defining 10 lines.
- Feature extraction is performed (image energy, mean and variance, were calculated and considered as features that are fed to mamdani-type fuzzy system for expression recognition).
Strengths
·
Average recognition
rate of expression is 92.3%.
·
Superiority
of proposed system to existing ones.
Advantages
- This system is capable of recognizing 6 basic human facial expressions that are happiness, surprise, anger, fear, disgust and sadness.
- System is simple & highly accurate.
- Experimental results on JAFFEE database indicate good performance of the developed technique.
Disadvantages
- It needs static image as input for giving expression as the output.
Future work
Will include emotion recognition based on considering more
facial regions, improving rules and combining other classifiers to the fuzzy
system and for better performance.
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