Facial Expression Recognition using Convolutional Neural Networks Based on Half Facial Sections

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Panamet Yanthitirat
Charun Sanrach
Soradech Krootjohn


This research presented Facial Expression Recognition by using Convolutional Neural Networks. The hypothesis for the research was that if the position of human facial organs is near symmetry with the nose actually centered, using half facial sections can analyze emotional expression recognition of the face. Facial recognition was divided into three emotional groups: negative emotion, normal emotion, and positive emotion which used data set from the competition FER2013 for training set and test set. The faces within the data set were divided into left and right half images. The researcher started with the selection of three different convoluted neural network structures, namely LeNet-5, Mememoji, and 3CNNs which were developed and compared the models to suit the classify of the left and right halves. Upon obtaining efficient models, the researcher blended models to make them suitable for classification of both images. The results of this merged model had the highest accuracy at 82.77%.

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