TY - GEN
T1 - On the importance of feature aggregation for face reconstruction
AU - Xu, Xiang
AU - Le, Ha
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
Acknowledgement: This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061-BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project EDGE awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. We are grateful for the support of the Center for Advanced Computing and Data Science at the University of Houston for assistance with the calculations carried out in this work.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/4
Y1 - 2019/3/4
N2 - The goal of this work is to identify principles of designing a deep neural network for 3D face reconstruction from a single image. To make the evaluation simple, we generated a synthetic dataset and used it for evaluation. We conducted extensive experiments using an end-to-end face reconstruction algorithm using E2FAR and its variants, and analyzed the reason why it can be successfully applied for 3D face reconstruction. From the comparative studies, we conclude that feature aggregation from different layers is a key point to training better neural networks for 3D face reconstruction. Based on these observations, a face reconstruction feature aggregation network (FR-FAN) is proposed, which obtains significant improvements compared with baselines on the synthetic validation set. We evaluate our model on existing popular indoor and in-the-wild 2D-3D datasets. Extensive experiments demonstrate that FR-FAN performs 16.50% and 9.54% better than E2FAR on BU-3DFE and JNU-3D, respectively. Finally, the sensitivity analysis we performed on controlled datasets demonstrates that our designed network is robust to large variations of pose, illumination, and expressions.
AB - The goal of this work is to identify principles of designing a deep neural network for 3D face reconstruction from a single image. To make the evaluation simple, we generated a synthetic dataset and used it for evaluation. We conducted extensive experiments using an end-to-end face reconstruction algorithm using E2FAR and its variants, and analyzed the reason why it can be successfully applied for 3D face reconstruction. From the comparative studies, we conclude that feature aggregation from different layers is a key point to training better neural networks for 3D face reconstruction. Based on these observations, a face reconstruction feature aggregation network (FR-FAN) is proposed, which obtains significant improvements compared with baselines on the synthetic validation set. We evaluate our model on existing popular indoor and in-the-wild 2D-3D datasets. Extensive experiments demonstrate that FR-FAN performs 16.50% and 9.54% better than E2FAR on BU-3DFE and JNU-3D, respectively. Finally, the sensitivity analysis we performed on controlled datasets demonstrates that our designed network is robust to large variations of pose, illumination, and expressions.
UR - http://www.scopus.com/inward/record.url?scp=85063568840&partnerID=8YFLogxK
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U2 - 10.1109/WACV.2019.00103
DO - 10.1109/WACV.2019.00103
M3 - Conference contribution
AN - SCOPUS:85063568840
T3 - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
SP - 922
EP - 931
BT - Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Y2 - 7 January 2019 through 11 January 2019
ER -