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MR-CapsNet: A Deep Learning Algorithm for Image-Based Head Pose Estimation on CapsNet
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DOI：10.1109/ACCESS.2021.3119615.png)
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Abstract
En 中文
Head pose estimation based on a single image is a challenging endeavor because of the complex background conditions and characteristics of the human face. In this report, we propose a Multi stage Regression-Capsule Network (MR-CapsNet) to predict head posture based on a single image input. In the study, we used the residual attention block and squeeze-and-excitation block to extract features in three levels. CapsNet overcomes the shortcomings of the traditional convolutional neural network and implements module aggregation to describe the spatial relationship of features after aggregation, in addition to realizing a compact and robust model using a multi-stage regression scheme. We tested our method on the AFLW2000 and BIWI datasets obtaining mean absolute errors of 4.26% and 3.95%, respectively. In addition, we discuss the accuracy of our method in the case of eye or mouth occlusion. The results of comprehensive experiments reveal that our method can accurately predict head posture.
Keywords:
Head
Feature extraction
Face recognition
Pose estimation
Magnetic heads
Training
Task analysis
Head pose estimation
multi-stage regression
squeeze-and-excitation block
capsule network
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W