Three-Stream Head Pose Estimation Algorithm Based on Multi-Stage Feature Fusion

Aiming at the problems of poor real-time performance and low recognition rate of existing head pose estimation algorithms in complex scenes, a three-stream head pose estimation algorithm based on multi-stage feature fusion is proposed. The algorithm has a multi-level output structure. Three different types of networks are used to extract features from the input image, and each branch has three stages. Each stage only needs to refine the features of previous stage. Feature map extracted at the same stage is generated by the feature fusion module, which effectively avoids the problem of feature loss. The feature extraction module selects the Ghost module as the feature extraction network, and uses model compression to reduce network parameters and computation while ensuring network accuracy. In order to extract more important and effective features, an efficient channel attention module ECA-Net is introduced to improve the accuracy of head pose estimation. Experimental results show that the proposed algorithm achieves excellent performance on both the AFLW2000 dataset and the BIWI dataset, with a model size of only 0.55 MB and a reduced MAE of 4.68 and 3.59 on the AFLW2000 and BIWI datasets respectively, compared to many current head pose estimation methods. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.