Emotion recognition for mental health prediction using AI techniques: An overview

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Sonali Singh
Navita Srivastava



Human facial expressions are a mirror of human thoughts, feelings and human mental states. Facial Emotion Recognition (FER) can provide a social advantage. It's like a form of silent communication. Emotion recognition technology will help to automatically detect the patient's emotions during illness and avoid external acts such as suicide, mental disorders or mental health problems. If we understand all the signs of emotions, we can solve many problems for human beings. Emotion recognition and detection is also useful for healthcare. Through emotional state recognition, we can get information about patients. Recognizing a patient's emotions for a specific disease using artificial intelligence techniques is a challenging task. This article presents recognition, detection and methods for mental health patients. Using artificial intelligence techniques with an emotion detection library and matching emotions to mental health. This article uses an emotional scale to show that there is a link between negative emotions and mental health problems. In this paper, she provided a comprehensive review of AI-based FER methodology, including datasets, feature extraction techniques, algorithms, and recent breakthroughs with their applications in facial expression recognition. In the future, all aspects of FER for different ages would significantly influence the health research community.


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Sonali Singh, Emotion recognition for mental health prediction using AI techniques: An overview

Department of Computer



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