Effifficient Face Mask Recognition Model Based on Deep Learning and Multi-machine Learning Classififiers
الباحث الأول:
Hassenien Ali Hussein
الباحثين الآخرين:
Firas Muneam Bachay, Saffa Jasim Mosa, Mohammed Hasan Abdulameer
المجلة:
Baghdad Science Journal
تاريخ النشر:
24 نوفمبر، 2025
مختصر البحث:
Safety in chemical and biological laboratories requires face masks. Nevertheless, the masked face images present
obstacles to accommodate the wide pose variations, low resolution, changes in size and incomplete cropping, and varying
emotive expres…
Safety in chemical and biological laboratories requires face masks. Nevertheless, the masked face images present
obstacles to accommodate the wide pose variations, low resolution, changes in size and incomplete cropping, and varying
emotive expressions, making feature extraction and recognition robust di cult. To decern between faces with proper,
improper, and no facemask, Deep Learning (the use of Convolutional Neural Networks (CNNs)) and classiers such
as SoftMax, Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) were used. The methodology utilized
the 2075 AR face images that contain various resolutions, orientations, and occlusions. The paper develops a Deep
Learning-based model utilizing CNNs and classiers to detect proper, improper and no facemask usage. The results were
promising, especially with occlusions in the moderated level: SoftMax with an accuracy of 88.41%, SVM with 80.19%
and KNN of 47.58%. This methodology will assist in addressing challenges in detecting masked faces especially the
wrong mask, which could help monitor compliance and ensure safety in other environments as well. The signicance
of the problem addressed is of utmost importance as it helps develop and ensure accurate detections of proper usage of
facemasks. This issue is critical for safety in the chemical and biological laboratories and has implications for compliant
enforcement, for safety, and for other industries especially those reliant on facial recognition technologies.
