Abstract:
Facial feature extraction is an essential step
in the face detection and facial expression
recognition frameworks. To develop a better
facial expression recognition system, a good
feature extraction method is needed. In this
paper, an efficient Facial Feature Extraction
method for recognizing four different expressions
such as neutral, happy, surprise and sad is
presented. In this study, adaptive canny operator
edge detection method is used to reduce the
computational complexity and improved the
accuracy of feature point location. To validate
the performance of the proposed feature
extraction, the generated features are classified
using Maximum Correlation Classifier (MCC).
The experimental results demonstrated that the
proposed feature extraction method could
generate significant facial features and these
features are able to be classified into each
expression. Our results also showed that the
proposed feature extraction method is more
efficient than Gabor wavelet edge detection
method.
