Facial expression recognition by using modified convolutional neural network (mcnn) and modified gabor filter
International Journal of Development Research
Facial expression recognition by using modified convolutional neural network (mcnn) and modified gabor filter
Received 14th August 2017; Received in revised form 29th September, 2017; Accepted 03rd October, 2017; Published online 29th November, 2017
Copyright ©2017, Lafta Raheem Ali et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Facial expression recognition is an important topic in an image processing and pattern recognition. The expressions work as a mirror for what happened in the human mind, enable individuals to understand what their peers feel. This paper suggest develop a system for recognizing facial expressions, modified Gabor filter has been use for feature extraction, then classifying the facial expressions based on these extracted features by using a modified Convolutional Neural Network (MCNN). the results of using MCNN with Modified Gabor was (93%-99%) accuracy rate with very less time for both multi and single face respectively We have gotten a very good result comparing to the other methods in the same fields (PCA and Gabor filter. This results with Extended Cohn Kanade (CK+) and JAFFE Databases.
