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

基于HSV+HOG特征和SVM的人脸口罩检测算法研究

作　　者：HE Yumin WANG Zhaohui GUO Siyu YAO Shipeng HU Xiangyang 何育民;汪朝辉;郭思宇;姚世鹏;胡象洋(西安建筑科技大学机电工程学院,陕西西安710055)

机构地区：[1]School of Mechanical and Electrical Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China

出　　处：《Journal of Measurement Science and Instrumentation》2022年第3期267-275,共9页测试科学与仪器（英文版）

基　　金：National Natural Science Foundation of China(No.519705449)。

摘　　要：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.

关 键 词：hue-saturation-value(HSV)features histogram of oriented gradient(HOG)features support vector machine(SVM) face mask detection feature point detection 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]