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Face mask detection algorithm based on HSV+HOG features and SVM 被引量:6
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作者 HE Yumin WANG Zhaohui +2 位作者 GUO Siyu YAO Shipeng HU Xiangyang 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第3期267-275,共9页
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 machine... 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. 展开更多
关键词 hue-saturation-value(HSV)features histogram of oriented gradient(hog)features support vector machine(SVM) face mask detection feature point detection
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结合聚合通道特征和双树复小波变换的手势识别 被引量:11
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作者 鲍文霞 解栋文 +1 位作者 朱明 梁栋 《中国图象图形学报》 CSCD 北大核心 2019年第7期1067-1075,共9页
目的针对目前手势识别方法受环境、光线、旋转、缩放、肤色等因素的影响,导致手势识别精度下降的问题,提出一种结合聚合通道特征(ACF)的手势检测和双树复小波变换(DTCWT)的复杂背景下手势识别方法。方法在手势图像预处理过程中引入聚合... 目的针对目前手势识别方法受环境、光线、旋转、缩放、肤色等因素的影响,导致手势识别精度下降的问题,提出一种结合聚合通道特征(ACF)的手势检测和双树复小波变换(DTCWT)的复杂背景下手势识别方法。方法在手势图像预处理过程中引入聚合通道特征,采用Adaboost分类器和非极大值抑制算法(NMS)进行目标手势的检测;利用DTCWT对目标手势图像进行多尺度多方向分解,对高低频系数的每一块分别提取方向梯度直方图(HOG)和局部二值模式(LBP)特征;最后融合各个方向上的高低频特征并通过支持向量机(SVM)进行分类识别。结果选取多个场景、多个对象、不同角度和距离的图像作为训练集,并标注区分前背景,对20种手势进行识别实验,并与传统的肤色检测、HOG特征手势识别、类-Hausdorff距离的手势识别算法进行了实验对比。在任意可承受范围内的光照、距离等情况下,该方法能够更准确实时地实现手势识别,平均精度达到95.1%。结论在图像预处理的情况下,聚合通道特征的引入能够准确检测手势,同时基于DTCWT的手势图像频域特征提取和再融合的方法有效地解决了传统普通图像的单特征识别方法在光线和复杂背景下识别精度不高的问题。 展开更多
关键词 聚合通道特征 双树复小波变换(DTCWT) 方向梯度直方图(hog)特征 二值模式(LBP)特征 特征融合 支持向量机(SVM)
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