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For many years, the emotion recognition task has remained one of the mostinteresting and important problems in the field of human-computer interaction.In this study, we consider the emotion recognition task as a classification aswell as a regression task by processing encoded emotions in different datasetsusing deep learning models. Our model combines convolutional neural network(CNN) with recurrent neural network (RNN) to predict dimensional emotions onvideo data. At the first step, CNN extracts feature vectors from video frames.In the second step, we fed these feature vectors to train RNN for exploitingthe temporal dynamics of video. Furthermore, we analyzed how each neuralnetwork contributes to the system's overall performance. The experiments areperformed on publicly available datasets including the largest modern Aff-Wild2database. It contains over sixty hours of video data. We discovered the problemof overfitting of the model on an unbalanced dataset with an illustrativeexample using confusion matrices. The problem is solved by downsamplingtechnique to balance the dataset. By significantly decreasing training data, webalance the dataset, thereby, the overall performance of the model is improved.Hence, the study qualitatively describes the abilities of deep learning modelsexploring enough amount of data to predict facial emotions. Our proposed methodis implemented using Tensorflow Keras.

Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network