A Framework for Recognition of Facial Expression Using HOG Features

Abstract

Recognition of facial expressions has been an important topic of study over the last several decades, and despite the advancements that have been made, it is still difficult to do because of the significant intra-class diversity. The handcrafted feature is used in traditional methods to address this issue. This feature is then preceded by a classifier that is trained using a database of pictures or videos. The majority of these works do quite well on datasets of photographs that were recorded in a controlled environment. However, they do not perform as well on datasets that are more difficult to work with since they include greater image variance and partial faces. The Histogram of Oriented Gradient (HOG) descriptor is the foundation for the strategy that is suggested in this study. During the initial step of the procedure, the input picture is pre-processed in order to locate the datum region, which assists in the extraction of the most relevant characteristics. After that, the Random Forest (RF) algorithm was employed as a classifier for facial expressions. The Japanese Female Facial Emotions Database (JAFFE) is used to assess our technique. The experimental findings demonstrated that the suggested method is accurate and effective in identifying facial expressions.