Recognition of Human Facial Expressions through the Application of Emerging Neural Networks - Kajal; Kanchan Saini; Neeraj Kumar (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
Emotion recognition based on facial expressions has long been a human strength, but developing an algorithm to do the same feat is a formidable challenge. Recent developments in computer vision and ML have made emotion detection in pictures a reality. For a long time, face detection has been available. The next logical step is to simulate the human brain’s expressions using video, electroencephalogram (EEG), or still images of the face. In order for contemporary AI systems to mimic and assess reactions from face, human emotion recognition is an urgent necessity. Whether it’s about identifying intent, promoting offerings, or security-related dangers, this may assist make educated judgments. Emotion recognition in photos or videos is easy for humans to do, but it’s a huge challenge for computers and calls for a plethora of image processing algorithms to extract features. This task may be accomplished with the help of several machine learning techniques. In order for machine learning to do any sort of detection or identification, training algorithms must first be developed and then tested on appropriate datasets. Facial emotion recognition via neural networks (NN) is a new method that we present in this article. Using real-life human emotions, the suggested technique achieves an unprecedented level of real-time emotion identification an average accuracy of 94%.