Multi-angle head pose classification with masks based on color texture analysis and stack generalization.
Concurr Comput
; : e6331, 2021 Apr 22.
Article
in English
| MEDLINE | ID: covidwho-1201885
ABSTRACT
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.
Full text:
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Collection:
International databases
Database:
MEDLINE
Language:
English
Journal:
Concurr Comput
Year:
2021
Document Type:
Article
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