Real-Time Masked Face Recognition Using FaceNet and Supervised Machine Learning

Adhinata, Faisal Dharma and Tanjung, Nia Annisa Ferani and Widayat, Widi and Pasfica, Gracia Rizka and Satura, Fadlan Raka Real-Time Masked Face Recognition Using FaceNet and Supervised Machine Learning. Proceedings of the 2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics.

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Abstract

The coronavirus pandemic has led to the implementation of health protocols such as the use of masks worldwide. Without exception, work activities also require the wearing of masks. This condition makes it difficult to recognize an individual’s identity because the mask covers half of the face, especially when the employee is present. The attendance system recognizes a face without a mask more accurately, in contrast, a masked face makes identity recognition inaccurate. Therefore, this study proposes a combination of facial feature extraction using FaceNet and several classification methods. The three supervised machine learning methods were evaluated, namely multiclass Support Vector Machine (SVM), K-Nearest Neighbor, and Random Forest. Furthermore, the masked face recognition system was evaluated using real-time video data to assess the accuracy and processing time of the video frame. The accuracy result on real-time video data using a combination of FaceNet with K-NN, multiclass SVM, or Random Forest of 96.03%, 96.15%, and 54.04% are obtained respectively and in processing time per frame of 0.056 s, 0.055 s, and 0.061 are obtained respectively. The results show that the combination of the FaceNet feature extraction method with multiclass SVM produces the best accuracy and data processing speed. In other words, this combination can reach 18 fps at real-time video processing. Based on these results, the proposed combined method is suitable for real-time masked face recognition. This study provides an overview of the masked face recognition method so that it can be a reference for the contactless attendance system in this pandemic era.

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