AI-Driven Micro-Expression Recognition for Early Mental Health Disorder

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]29/09/2025 at 11:47 AM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 40 49

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    Charu Srivastav, Aadya Mishra, Rinku Raheja, Prabhash Chandra Pathak,

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  1. Research Scholar, Research Scholar, Research Scholar, Professor, Department of Computer Science, National P.G. College Lucknow, Department of Computer Science, National P.G. College Lucknow, Department of Computer Applications, Babu Banarasi Das University Lucknow, Department of Computer Applications, Babu Banarasi Das University Lucknow, Uttar Pradesh, Uttar Pradesh, Uttar Pradesh, Uttar Pradesh, India, India, India, India

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Abstract

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nMental health conditions like anxiety and depression are often undiagnosed because the usual diagnostic methods based on basic regular instruments like questionnaires and clinical interviews have some limitations in them. They are not objective often and may not catch the initial signs of psychological distress. Micro-expressions have become valid measures of repressed or unconscious emotions and can provide greater insight into someone’s mental condition. Also, identification and interpretation of these micro-level cues in real-time is very challenging and usually needs the assistance of AI-powered technologies. This research examines how artificial intelligence, and specifically deep learning structures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, can improve the micro-expression analysis to aid early mental health disorder diagnosis. The findings of AI-generated analysis are compared with conventional approaches using accuracy, speed, and scalability as measures. This model will also discuss the value of including multimodal data, like voice tone and physiological signals. This will enhance diagnostic accuracy further. Even though early results are encouraging but ethical considerations about data privacy, fairness, and openness need to be addressed. This study seeks to contribute to AI systems that enable timely, accurate, and ethically robust mental health assessment.nn

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Keywords: Artificial intelligence (AI), micro-expression recognition, mental health diagnosis, deep learning

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Mobile Computing, Communications & Mobile Networks ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Mobile Computing, Communications & Mobile Networks (jomccmn)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nCharu Srivastav, Aadya Mishra, Rinku Raheja, Prabhash Chandra Pathak. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]AI-Driven Micro-Expression Recognition for Early Mental Health Disorder[/if 2584]. Journal of Mobile Computing, Communications & Mobile Networks. 29/09/2025; 12(03):40-49.

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How to cite this URL:
nCharu Srivastav, Aadya Mishra, Rinku Raheja, Prabhash Chandra Pathak. [if 2584 equals=”][226 striphtml=1][else]AI-Driven Micro-Expression Recognition for Early Mental Health Disorder[/if 2584]. Journal of Mobile Computing, Communications & Mobile Networks. 29/09/2025; 12(03):40-49. Available from: https://journals.stmjournals.com/jomccmn/article=29/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 13/08/2025
Accepted 20/09/2025
Published 29/09/2025
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Publication Time 47 Days

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