Train Your Own Neural Network for Facial Expression Recognition Using TensorFlow, CNN and Keras

Facial expression recognition is one of the most important aspects of human-computer interaction, based on which the system needs to perceive and respond to the emotions expressed by a human. This paper presents a facial expression recognition system that makes use of the convolutional neural network, using TensorFlow and Keras for its implementation. It categories emotions included in the FER2013 dataset consisting of 48x48 pixel grayscale images labeled with seven emotional categories: Angry, Disgust, Fear, Happy, Sad, Surprise, and Neutral. It basically comprises steps like image preprocessing, designing and training a CNN in order to hierarchically extract features from data, and using some metrics such as accuracy, confusion matrix, and classification report regarding model performance. The results showed a high degree of accuracy in classifying emotions, hence proving the effectiveness of deep learning techniques in this domain. The following findings highlight the model applications that have contributed to affective computing, improving customer experience, and interactive systems; these depict a big move in emotion recognition technologies.