A multi-class framework for face mask compliance detection using lightweight deep learning models

The use of face mask compliance regarding compliance monitoring is important in health sensitive and surveillance driven environments. This paper has introduced a multi-class face mask compliance detection system in the form of a Deep Convolutional Neural Network (DCNN) and advanced MobileNetV3 model. The proposed system is also based on a more difficult three-class problem (correct mask use, improper mask use, and no mask) as opposed to the traditional binary mask detection methods. A balanced custom curated dataset from KAGGLE of 2,079 images was experimented on, manually checked to be of quality, consistent, and equally represent all the classes, including improper mask-wearing cases that are usually underrepresented in prior studies.In the three-class task, the standard MobileNetV3 architecture was recast by substituting the final classification head with a lightweight fully connected layer of 256 neurons paired with a three-neuron softmax output layer. Squeeze-and-Excitation (SE) blocks were added to make the channel-wise recalibration of features and better separation of visually related classes. Stratified hold-out validation strategy (a 70:15:15 train-validation-test split) was employed to guarantee that performance is assessed by reliably generalizing. we were able to perform performance evaluation through accuracy, precision, recall, F1-score, specificity, Cohen Kappa, and confusion matrix analysis. Besides the comparison of DCNN, the given model was compared with the representative lightweight and state-of-the-art frameworks that include the MobileNetV2-, VGG16-, and YOLO-based models. MobileNetV3 obtained 98.90% accuracy and F1-score of 0.989, which proves to be competitive or even better in a more challenging multi-class compliance environment. In the future, the emphasis will be put on cross-dataset validation, real-time video monitoring, and the integration of explainable AI in order to improve practical implementation.