Facial expression recognition: convolutional attentional masking network and ensemble approach
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Facial expression plays a signiﬁcant role in human communication. The necessity of recognizing facial expression is increasing rapidly as it can be implemented in various important ﬁelds such as in human-computer interactions, medical care, autonomous transportation systems etc. The facial expression detection has been accomplished by the analysis of convolutional neural networks on the micromotors and action units. In this thesis, we have introduced a new variant of residual architecture named CAMnet which uses the split attentional module and the masking module mechanisms simultaneously. Also, the model performs better compared to other models without using any pretrained weights on small dataset like FER2013. Additionally, along with the CAMnet an ensemble model has been implemented and we have achieved 76.12% accuracy on the FER2013 test set.