文献类型：期刊文献

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, LJ[1];Li, WF[2]

机构：[1]Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China;[2]Wave Grp, Cognit Comp Technol Joint Lab, Beijing, Peoples R China;[3]Beijing Key Lab Semicond Neural Network Intellige, Beijing, Peoples R China;[4]Shenzhen Wave Kingdom Co Ltd, Shenzhen, Peoples R China;[5]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China

第一机构：Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China;[2]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：0

外文期刊名：CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000642057400001)】；

基金：This work was supported by the National Natural Science Foundation of China (Nos. 61901436 and 61972040) and the Premium Funding Project for Academic Human Resources Development in Beijing Union University (No. BPHR2020AZ03).

语种：英文

外文关键词：color space conversion; head pose classification; line portrait; stacked generalization

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.