Facial Expression Recognition for Mental Health Monitoring

Anxiety, depression, and stress are examples of mental health issues that have become major global health concerns, impacting millions of people globally. Early identification and ongoing monitoring of mental health issues are now essential due to the rising need for prompt and efficient mental health care. Conventional techniques for evaluating mental health, such selfreport questionnaires and clinical interviews, frequently depend on subjective information and are therefore open to bias or inconsistent results. More objective, timely, and non-invasive techniques for monitoring mental health are therefore required. As per WHO Mental health issues encompass a range of mental states and illnesses, including psychosocial impairments and mental disorders, that are linked to considerable suffering, impaired functioning, or self-harm risk. 9 million individuals worldwide suffered from a mental illness in 2019, with anxiety and depression being the most prevalent. Facial expression recognition (FER) has drawn a lot of interest in this area as a potentially useful method for tracking psychological and emotional states. Facial expressions may be useful indications of an individual's emotional state because they are intimately related to it. An extensive analysis of contactless sensing techniques for tracking mental health is provided in this article. It looks at published studies that employ contactless sensing techniques to forecast mental health conditions.