Facial Emotion Detection: A CNN and Transfer Learning Approached Facial Emotion Detection System

Abstract

Emotions of a human plays an important role in human life shaping the thoughts, behaviours and relationships of a person. It is helpful in responding in different situations in life appropriately and quickly like in taking decisions and solving problems. Because of negative emotions people feel some of them take negative decisions and actions which harms towards their lives. With the modern busy lifestyle many people struggle with depression and stress due to the work load they have to handle in both in their work life and personal life. So having the capability to detecting emotions and understanding patterns through it can make a person to prevent being negative and live a positive mindset in life. This research Focuses on developing a strong model which deals with accurately detecting and classifying human emotions from facial expressions. The methodology basically functions with CNN architecture and transfer learning approach using MobileNet model. This approach mostly benefited from the existing knowledge provided with MobileNet contributing to the model's efficiency and accuracy the system was praying to identify nine distinct emotions angry, confused, disgusted, fearful, happy ,neutral, sad, shy and surprised. This model achieved 82% accuracy in recognizing these emotions demonstrating capability at integrating such technologies into mental healthcare strategies. The findings highlight the potential of automated facial motion analysis as an important tool for early detection and therapeutic interventions for various mental health conditions