Facial Expression Recognition System

Facial gestures help to understand what is really going on in a visual way in someone's mind. A different meaning is expressed by different expressions. Facial expression recognition system plays an important role in the field of machine learning and deep learning; it helps us to understand a deeper meaning of human–machine interaction. Recognition of human expression with high accuracy is still a difficult task for computers. Facial expression recognition is categorized into four stages they are preprocessing, registration of face, feature extraction of face, and classification of expressions. It is used in several applications such as understanding mental disorders, analyzing what is going on in a human’s mind, lie detection, etc. Here, a convolutional neural network is utilized which is very effective for image processing. The data used is supervised data. The FER2013 dataset is used for analysis and popular deep learning frameworks such as Keras and OpenCV for the detection of facial expressions in an accurate manner.