Single Camera Masked Face Identification

Abstract

In light of the novel Covid-19 pandemic, wearing masks has been declared mandatory in several institutions and public places for its widespread prevention and public health safety. Under given circumstances, person identification for security purposes including smart-phones face unlock has been a challenging task since the previous practices including both the human authentication by a person as well as by face recognition systems have heavily relied on complete facial features. However, the emergence of large datasets of masked images led to the rapid development of occluded face detection techniques. This paper focuses on single camera masked face detection and identification via the following two approaches: (i) single-step pre-trained YOLO-face/trained YOLOv3 model on the set of known individuals; and (ii) two-step process having pre-trained one stage feature pyramid detector network RetinaFace for localizing masked faces and VGGFace2 that generates facial feature vectors for efficient mask face verification. The dataset employed consists of real-world video examples comprising of 7 individuals with various orientations, illuminations, and occlusions. Experimental results show that RetinaFace and VGGFace2 achieve state-of-the-art results of 92.7% on overall performance, 98.1% face detection, and 94.5% face verification accuracy respectively in 1:1 face mask verification on our custom dataset.

Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition

“…Future research can continue to calculate processing time when using video data as testing data. The results of this study were better than previous studies [12], with an accuracy of 94.5%. In other words, the accuracy increased by 0.92% to 95.42%.…”

“…Previous research related to masked face recognition using deep learning resulted in an accuracy value of 94.5% [12]. This result is not quite optimal for accuracy.…”

Face mask recogniser using image processing and computer vision approach

“…In the method proposed in [ 15 , 16 , 17 ], MobileNetV2 pre-trained classifier is used for the purpose of classification. A Kaggle dataset with real world persons with masks is used.…”

Deep Learning Approach for Masked Face Identification

“…Aswal et al [19] presented two techniques that are used to recognize and identify masked faces. Using a single camera: A two-step process with a pre-trained one-stage feature pyramid detector network RetinaFace for localizing masked faces and VGGFace2 that generates facial feature vectors for efficient mask face verification; and (ii) a single-step pre-trained YOLO-face/trained YOLOv3 model on the set of known individuals.…”