A Facial Emotion Detection and Classification System using Convoluted Neural Networks

The ability to understand facial expressions is an important part of nonverbal communication. The value in understanding facial expressions is to gather information about how the other person is feeling and guide our interaction accordingly. A person's ability to interpret emotions is very important for Effective communication. Recent researches show that emotional states and motivation directly or indirectly influences of student's learning process. This work is however a plunge into how systems can correctly detect recognize and classify human (Students) facial emotional expression through various image sensors, using Convolutional Neural Network (CNN). Dataset containing 28821 Face images were acquired. All images were used for training and testing using Convolutional Neural Network algorithm implemented in MATLAB software. 80% of the image dataset were used in training the system, while 20% were used for testing the system. The Trained CNN classifier classify image emotions using the Adam optimizer for higher accuracy.