Expression Reognition Survey through Multi-Modal Data Analytics

Abstract

In the field of computer vision, capturing human expression from a video is essential for a variety of time-sensitive applications including driver safety and education. Because there are various models of expression (e.g., speech, face, gesture, etc. ), Human Expression Recognition can be done in a variety of methods. In this work, we look at various strategies for recognizing facial expressions. We primarily concentrated on two models in this review: speech signal and facial image. The entrance survey is conducted in two steps, according to the generic approach of human expression recognition. Our feature extraction approaches were divided into two categories: audio features and facial features. The review found different classifiers helped to decide the best framing of the markov chain for a model. We also looked at certain multi-modal expression recognition algorithms that used the multi-modal notion at various stages, such as feature fusion and decision fusion. We also looked at several databases that have been used by previous studies in addition to these approaches. A full-fledged comparison is also provided to completely show the review.