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9D Rotation Representation-SVD Fusion with Deep Learning for Unconstrained Head Pose Estimation Cover

9D Rotation Representation-SVD Fusion with Deep Learning for Unconstrained Head Pose Estimation

By: Jiaqi Lyu and  Changyuan Wang  
Open Access
|Sep 2024

Figures & Tables

Figure 1.

Overview of the proposed method
Overview of the proposed method

Figure 2.

MBConv
MBConv

Figure 3.

Fused-MBConv
Fused-MBConv

Figure 4.

Image samples from 300W-LP dataset with different rotation representations
Image samples from 300W-LP dataset with different rotation representations

Figure 5.

Example images of Euler angle visualization using rotation matrix transformation from AFLW2000 dataset
Example images of Euler angle visualization using rotation matrix transformation from AFLW2000 dataset

Comparison of the MAE between L2 and geodesic LOSS

AFLW2000BIWI70/30 BIWI
Loss functionMAEMAEMAE
L2 Loss3.903.922.71
Geodesic Loss3.853.732.50

Comparisons with state-of-the-art methods on the AFLW2000 and BIWI dataset

AFLW2000BIWI
ModelsYawPitchRollMAEYawPitchRollMAE
HopeNet[4]6.406.535.396.114.545.153.374.36
FSA-Net[8]4.506.084.645.074.645.613.574.61
HPE[6]4.806.184.875.283.125.184.574.29
QuatNet[5]3.975.623.924.502.945.494.014.15
WHENet[7]5.116.244.925.423.994.393.063.81
TriNet[9]4.045.774.204.674.114.763.053.97
FDN[10]3.785.613.884.424.524.702.563.93
6DRepNet[18]3.634.913.373.973.244.482.683.47
9D-EfficientNet3.574.693.283.854.084.172.943.73

Comparison of MAE between ResNet and EfficientNetV2 backbone networks

AFLW2000BIWI70/30 BIWI
ModelsMAEMAEMAE
ResNetl84.373.702.64
EfficientNetV2-S3.853.732.50

EfficientNetV2-S architecture

StageOperationStride#Channels#Layers
0Conv3x32241
1Fused-MBConv1,3x31242
2Fused-MBConv4,3x32484
3Fused-MBConv4,3x32644
4MBConv4,3x3,SE0.2521286
5MBConv6,3x3,SE0.2511609
6MBConv6,3x3,SE0.25225615
7Conv 1x1&Pooling&FC-12801

Euler error comparisons with state-of-the-art methods on the 70/30 BIWI dataset

BIWI
ModelsYawPitchRollMAE
HopeNet[4]3.293.393.003.23
FSA-Net[8]2.894.293.603.60
TriNet[9]2.933.042.442.80
FDN[10]3.003.982.883.29
MDFNet[20]2.993.682.993.22
DDD-Pose[21]3.042.942.432.80
6DRepNet[18]2.692.922.362.66
9D-EfficientNet2.622.362.512.50

Comparison of parameters and FLOPs between 6DRepNet and our method

ModelsParamsFLOPs
6DRepNet43.752M9.844G
9D-EfficientNet20.189M2.901G
Language: English
Page range: 62 - 68
Published on: Sep 30, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Jiaqi Lyu, Changyuan Wang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.