Journal
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW)
Volume -, Issue -, Pages 1628-1636Publisher
IEEE COMPUTER SOC
DOI: 10.1109/ICCVW.2019.00202
Keywords
-
Ask authors/readers for more resources
Facial expression recognition (FER) aims to encode expression information from faces. Previous studies often hold the assumption that human subjects should properly face the camera. Such a laboratory-controlled condition, however, is too rigid for in-wide applications. To tackle this issue, we propose a single image facial expression recognition method that is robust to face orientation and light conditions. We achieved this by proposing a novel face re centralization method by reconstructing a 3D face model from a single image. We then propose a novel end-to-end deep neural network that utilizes both re-centralized 3D model and landmarks for FER task. A comprehensive evaluation on three real-world datasets illustrates that the proposed model outperforms the state-of-the-art techniques in both large-scale and small-scale datasets. The superiority of our model on effectiveness and robustness is also demonstrated in both laboratory conditions and wild images.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available