Facial expression is non-verbal communication which implies appearance on the face, arm movements or voice intonation conveys feelings about something without utilizing words. There are numerous uses for facial expressions. Recognition of facial expressions draws more attention to a variety of disciplines, including Computer science, Biotechnology, Psychology, Chemical and Pharmaceutical science. The facial expressions used in Human Computer Interaction (HCI) research to improve outcomes. Accurate emotional feature extraction is made possible via facial expression recognition. Facial expressions recognition approaches in static images do not fully consider the features of facial organs and muscle movements, which are static and dynamic, as well as the geometric and appearance qualities of facial expressions. This limitation is solved by using patch-based 3D Gabor feature extraction, selecting key patches, and key distance features obtained by carrying out patch matching operations. Test results produce promising results under Correct Recognition Rate (CRR), significant performance improvements in consideration of facial features and muscle movements, reduced face registration errors, and faster processing time. According to the difference in state-of-the-art performance, the proposed approach gave the highest CRR on the JAFFE and Cohn-Kanade AU-Coded Facial Expression database which is conclusive.

Automated facial expression recognition system, face detection, emotion detection, and human-computer interaction.