Emotion Analysis from Facial Expressions Using Convolutional Neural Networks

In order to better understand human behavior, the emotional content of human facial expressions needs to be accurately analyzed and interpreted. While the perception of faces and facial expressions is a natural skill for humans, it still poses great challenges for computer systems. These difficulties result from the non-uniformity of the human face and differences in conditions such as lighting, shadows, face pose and orientation. Deep learning models, especially Convolutional Neural Networks (CNNs), have great potential to deal with these challenges due to their powerful automatic feature extraction and computational efficiency. In this study, a CNN model is proposed to classify seven different emotions (angry, disgust, fear, happy, sadness, surprise and neutral) using the FER-2013 dataset. With the proposed model, 70.62% accuracy on the training data and 70% on the test data has been achieved. The loss value was found to be 0.80 at the training stage and 0.86 at the testing stage.