Humans have an uncanny ability to detect and recognise emotions, which
is being researched for use in computerization. Face emotion prediction
remains a difficult field of study in spite of wide applications due to
its subject dependency. Innovative method of using the ensemble
classifier for the real time face emotion prediction using base
classifiers as Deep CNN models is proposed in this paper. The deep
learning algorithms are used as level 1 base classifiers. The imbalance
dataset CK+ and small dataset JAFFE are enhanced synthetically by image
augmentation method. At level 2, a meta classifier that is a fusion of
majority and relative voting techniques is utilized to improve the
accuracy of individual emotions. The proposed technique’s overall
performance is evaluated and validated using randomly selected face
emotion images from the internet with improved overall accuracy. The
proposed ensemble fusion technique NCL is used for cross validation on
FER2013 dataset.
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