Efficient Facial Expression Recognition Using Deep Learning Techniques

Abstract

Facial expression recognition (FER) is an important topic in the field of computer vision and artificial intelligence due to its potential in academic and business. The authors implement deep-learning-based FER approaches that use deep networks to allow end-to-end learning. It focuses on developing a cutting-edge hybrid deep-learning approach that combines a convolutional neural network (CNN) for the prediction and a convolutional neural network (CNN) for the classification. This chapter proposes a new methodology to analyze and implement a model to predict facial expression from a sequence of images. Considering the linguistic and psychological contemplations, an intermediary symbolic illustration is developed. Using a large set of image sequences recognition of six facial expressions is demonstrated. This analysis can fill in as a manual to novices in the field of FER, giving essential information and an overall comprehension of the most recent best in class contemplates, just as to experienced analysts searching for beneficial bearings for future work.