Face mask detection algorithm based on HSV+HOG features and SVM

To automatically detecting whether a person is wearing mask properly, we propose a face mask detection algorithm based on hue-saturation-value (HSV)+histogram of oriented gradient (HOG) features and support vector machines (SVM). Firstly, human face and five feature points are detected with RetinaFace face detection algorithm. The feature points are used to locate to mouth and nose region, and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not. Secondly, RetinaFace is used to locate to nasal tip area of face, and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area, and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results. Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%, and the accuracy of detecting whether mask is worn correctly can reach 87.55%, which verifies the feasibility of the algorithm.