Emotion recognition using machine learning: Opportunities and challenges for supporting those with autism or depression

Facial recognition technology, driven by advancements in machine learning, has become integral to various applications, from mobile security to access control. Recent developments in deep learning, particularly through convolutional and recurrent neural networks, have paved the way for enhanced emotion recognition capabilities. This paper explores the potential of facial emotion recognition (FER) and voice emotion recognition technologies, assessing their current state and future prospects. While FER techniques have evolved from traditional methods to deep learning approaches, and voice emotion recognition has shown promising results, challenges remain due to the inherent variability in human facial expressions and vocal characteristics. The paper further examines the impact of emotion recognition technologies on mental health, highlighting their potential to provide significant support to individuals with mental health disorders. By addressing the accuracy and application of these technologies, the research underscores the importance of continued innovation and careful implementation to maximize their benefits and mitigate potential risks. Ultimately, emotion recognition provides promising avenues for supporting those with mental illnesses. Aimed at fostering more personalized and effective interventions, these technologies hold the potential to revolutionize mental health care and improve the overall well-being of individuals.