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Impact Factor : 8.14

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Paper Title: Real Time Face Mask Detection System Using AI and Machine Learning
Authors Name: Mohammad Mateen Mousoof , Mohammad Hamza Faisal , Mohd Akkas , Ms.Tabassum
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IJRTI_209890
Published Paper Id: IJRTI2602093
Published In: Volume 11 Issue 2, February-2026
DOI: https://doi.org/10.56975/ijrti.v11i2.209890
Abstract: The global paradigm shift in public health monitoring has cemented automated face mask detection as a critical component of smart-city infrastructure and bio-surveillance ecosystems. While initially catalyzed by pandemic-era mandates, the enduring need for non-intrusive compliance tracking in high-density public spaces demands highly accurate, privacy-preserving, and computationally efficient computer vision solutions. Despite significant advancements in deep learning, existing object detection frameworks frequently struggle with the dichotomy of high-precision classification and the stringent low-latency constraints of edge-computing devices. Heavyweight models suffer from processing bottlenecks on standard closed-circuit television (CCTV) hardware, whereas overly simplified models fail to account for complex optical variables such as dynamic lighting, dense crowd occlusion, and diverse facial orientations. To address these critical limitations, this paper proposes a highly optimized, real-time face mask detection framework that synergizes the rapid localization capabilities of the You Only Look Once (YOLO) architecture with the lightweight feature extraction efficiency of a MobileNetV2 backbone. By mathematically streamlining convolutional operations and bounding-box regression, the proposed hybrid pipeline is engineered for seamless deployment on low-cost Internet of Things (IoT) nodes without relying on cloud-based processing. Furthermore, this research systematically synthesizes 25 pivotal studies, mapping the evolutionary trajectory of facial occlusion detection from early heuristic classifiers to modern single-stage convolutional neural networks, thereby providing a comprehensive theoretical foundation. Empirical evaluations on a withheld testing dataset demonstrate that the MobileNetV2-backed classifier achieves an exceptional validation accuracy of 97.5%. Concurrently, the system sustains an average inference speed of 45 frames per second on commercial graphical processing units, unequivocally satisfying real-time processing thresholds. Ultimately, this framework provides a scalable, robust blueprint for integrating localized artificial intelligence into permanent, proactive public health architectures.
Keywords: Real-Time Face Mask Detection, Deep Learning, YOLO Architecture, Edge Computing, Bio-Surveillance, Sustainable Development Goal 3, SDG 9 (Innovation and Infrastructure), Smart Cities (SDG 11).
Cite Article: "Real Time Face Mask Detection System Using AI and Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.a692-a706, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602093.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2602093
Registration ID:209890
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i2.209890
Page No: a692-a706
Country: LUCKNOW, UTTAR PRADESH, INDIA
Research Area: Computer Science & Technology 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602093
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602093
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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