Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning

• Published in: Applied sciences Vol. 13; no. 2; p. 935
• Main Authors: Kumar, B. Anil, Bansal, Mohan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023
• Subjects: Algorithms; Artificial intelligence; Biometrics; Caffe-MobileNetV2 model; Classification; Coronaviruses; COVID-19; Datasets; Deep learning; deep neural network; Ear; Epidemics; Eye (anatomy); face detection; face mask classification; Face recognition; Facial recognition technology; feature extraction; Forehead; Image classification; Localization; Masks; Medical research; Neural networks; Nose; Performance evaluation; Protective equipment; Real time; Research methodology; Task complexity; Transfer learning; Visibility; Webcams
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13020935

Face detection systems have generally been used primarily for non-masked faces, which include relevant facial characteristics such as the ears, chin, lips, nose, and eyes. Masks are necessary to cover faces in many situations, such as pandemics, crime scenes, medical settings, high pollution, and laboratories. The COVID-19 epidemic has increased the requirement for people to use protective face masks in public places. Analysis of face detection technology is crucial with blocked faces, which typically have visibility only in the periocular area and above. This paper aims to implement a model on complex data, i.e., by taking tasks for the face detection of people from the photo and in real-time video images with and without a mask. This task is implemented based on the features around their eyes, ears, nose, and forehead by using the original masked and unmasked images to form a baseline for face detection. The idea of performing such a task is by using the Caffe-MobileNetV2 (CMNV2) model for feature extraction and masked image classification. The convolutional architecture for the fast feature embedding Caffe model is used as a face detector, and the MobileNetV2 is used for mask identification. In this work, five different layers are added to the pre-trained MobileNetV2 architecture for better classification accuracy with fewer training parameters for the given data for face mask detection. Experimental results revealed that the proposed methodology performed well, with an accuracy of 99.64% on photo images and good accuracy on real-time video images. Other metrics show that the model outperforms previous models with a precision of 100%, recall of 99.28%, f1-score of 99.64%, and an error rate of 0.36%. Face mask detection was originally a form of computing application, but it is now widely used in other technological areas such as smartphones and artificial intelligence. Computer-based masked-face detection belongs in the category of biometrics, since it includes using a person’s unique features to identify them with a mask on.