Emotion Classification of Facial Images Using Machine Learning Models

Abstract

Visual sentiment analysis, which studies the emotional response of humans on visual stimuli such as images and videos, has been an interesting and challenging problem. It tries to understand the high-level content of visual data. The success of current models can be attributed to the development of robust algorithms from computer vision. Most of the existing models try to solve the problem by proposing either robust features or more complex models. In particular, visual features from the whole image or video are the main proposed inputs. Little attention has been paid to local areas, which we believe is pretty relevant to human’s emotional response to the whole image. Application of image recognition to find people in images and analyze their sentiments or emotions. This project uses the CNN algorithm to perform that task. Given an image, it would search for faces, identify them, put a rectangle in their positions and describe the emotion found and emoji is displayed.