Published Paper Details:

Facial expression detection is a critical component of the application designed to address mental health issues. By leveraging cutting-edge machine learning algorithms, system can analyse facial expressions to detect early signs of Facial , anxiety, and other mental health concerns. The approach involves gathering data from various sources, including social media networks, to train our models and improve their accuracy. System employs sophisticated techniques such as image and video processing to analyse facial gestures and expressions. Key facial characteristics including the lips, nose, hands, and eyes can help us recognize small clues that represent various emotional states. These cues include variations in muscle movements, changes in facial symmetry, and shifts in skin tone. Proposed approach makes use of an extensive emotion expression system that classifies facial expressions into several emotional states, such as neutral, happy, sad, and angry. By precisely recognizing these emotions, system can give clients bits of knowledge into their psychological prosperity and accommodate them with identifying possible problems early on. Through extensive analysis of facial expressions and behavioural patterns, the application can offer personalized recommendations and assistance to those dealing with problems related to mental health. By use of the identification and analysis of facial emotions, system empower the clients to proactively pursue improving their psychological well-being and seeking appropriate assistance when needed. In general, systems goal to enable early identification and analysis of mental health concerns is greatly aided by system's facial expression detection technology, which in turn helps people live longer, better lives.