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.

中文翻译：

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基于颜色纹理分析和堆栈泛化的带掩模的多角度头部姿势分类


头部姿态分类是人脸识别预处理过程的重要组成部分，可以独立解决多角度相关的应用问题。但是，由于COVID-19冠状病毒大流行的影响，越来越多的人戴上口罩来保护自己，口罩覆盖了面部的大部分区域。这极大地影响了头部姿势分类的性能。因此，本文提出了一种对佩戴口罩的头部姿势进行分类的方法。该方法侧重于有助于头部姿势分类的信息。首先，通过颜色空间的转换提取HSV颜色空间的H通道图像。然后利用线条肖像提取人脸轮廓线，结合灰度图像训练卷积神经网络提取特征。最后利用堆叠泛化技术对三个分类器的输出进行融合，得到最终的分类结果。 MAFA数据集上的结果表明，与当前的先进算法相比，我们的方法的准确率在正面为94.14％，在更侧为86.58％，在侧面为90.93％，具有更好的性能。