6 th International Conference on Computer Science and Engineering, Ankara, Türkiye, 15 - 17 Eylül 2021, cilt.6, ss.1-5
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.