Non-verbal communication, such as facial expression, also includes the use of body language and vocal inflection to communicate emotion. Facial expressions may be used to many different purposes. Computer science, Biotechnology, Psychology, Chemistry, and Pharmacy all get additional interest as a result of face expression recognition. Expressions used in HCI studies to better understand human-computer interactions. Facial expression identification paves the way for precise extraction of emotional characteristics. Static picture facial expression identification methods neglect the dynamic and static properties of facial organs and muscle movements, as well as the geometry and visual elements of facial emotions. By performing patch matching procedures and choosing critical patches, we are able to overcome this constraint utilising extracted 3D Gabor features based on patches. Positive findings from the testing phase include an increased Correct Recognition Rate (CRR), better performance when taking facial characteristics and bodily movements into account, fewer incorrect face registrations, and shorter processing time. The suggested method provided the greatest CRR on the JAFFE and Cohn-Kanade AU-Coded Facial Expression datasets, establishing a clear advantage above state-of-the-art methods.