文献类型：会议论文

英文题名：Multi-angle head pose classification with masks based on color texture analysis and stack generalization

作者：Li, Shuang[1,2,3]; Dong, Xiaoli[1,2,3]; Shi, Yuan[2,4]; Lu, Baoli[1,3]; Sun, Linjun[1,2,3]; Li, Wenfa[5]

第一作者：Li, Shuang

通讯作者：Sun, Linjun

机构：[1] Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China; [2] Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing, China; [3] Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, Beijing, China; [4] Shenzhen Wave Kingdom Co., Ltd., Shenzhen, China; [5] College of Robotics, Beijing Union University, Beijing, China

第一机构：Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China

语种：英文

外文关键词：Classification (of information) - Color - Convolutional neural networks - Coronavirus - Textures - Wear of materials

摘要：Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance. ? 2021 John Wiley & Sons, Ltd.