Deep learning

Psychiatry

Sociology

Pattern recognition (psychology)

Artificial intelligence

Psychology

Emotion classification

Disgust

Happiness

Anger

Face (sociological concept)

Sadness

Gabor filter

Surprise

Facial recognition system

Social science

Computer science

Feature extraction

Social psychology

Convolutional neural network

The emotions evolved in human face have a great influence on decisions and arguments about various subjects. In psychological theory, emotional states of a person can be classified into six main categories: surprise, fear, disgust, anger, happiness and sadness. Automatic extraction of these emotions from the face images can help in human computer interaction as well as many other applications. Machine learning algorithms and especially deep neural network can learn complex features and classify the extracted patterns. In this paper, a deep learning based framework is proposed for human emotion recognition. The proposed framework uses the Gabor filters for feature extraction and then a Convolutional Neural Network (CNN) for classification. The experimental results show that the proposed methodology increases both of the speed training process of CNN and the recognition accuracy.

Fast Facial emotion recognition Using Convolutional Neural Networks and Gabor Filters