Affective Computing in Mental Health: The Role of Facial Expression Recognition – Expanding the Landscape of Emotional Understanding
Keywords:
Affective Computing, Facial Expression RecognitionAbstract
Affective computing, a rapidly evolving interdisciplinary field, leverages technological advancements to discern and interpret the intricate nuances of human emotions, thereby offering invaluable insights into the realm of mental health monitoring and intervention. This research paper delves into the pivotal role of Facial Expression Recognition (FER) as a salient instrument in the diagnosis, treatment, and ongoing management of mental health disorders. By dissecting the intricate physiological and psychological underpinnings of facial expressions, we analyze the potential of advanced FER systems to augment traditional mental health evaluation methodologies, providing real-time feedback and facilitating timely interventions. The continuous refinement of machine learning algorithms and computer vision techniques has significantly enhanced the accuracy and operational efficiency of FER systems, thereby broadening their applicability across diverse settings, including teletherapy platforms, clinical assessments, and personalized well-being applications. This paper further explores the inherent complexities and ethical considerations associated with FER technology, specifically addressing concerns surrounding privacy, data security, the potential for algorithmic bias, and the risk of misinterpretation. Synthesizing current research, we posit that FER holds significant promise as a meaningful contributor to the proactive care of individual mental health through continuous emotional monitoring and nuanced understanding of emotional states
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Copyright (c) 2024 S. Veerakannan (Author)
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