The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
Abstract
Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. We used our model's controllability to verify the importance of realistic illumination and shape variations in eye-region training data. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild.
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Cites
Eye Tracking and Eye-Based Human–Computer Interaction
Majaranta, P., & Bulling, A. (2014) · Human–Computer Interaction Series · doi:10.1007/978-1-4471-6392-3_3 · Cited by 530
Self-citationConstrained Local Neural Fields for Robust Facial Landmark Detection in the Wild
Baltrusaitis, T., Robinson, P., & Morency, L.-P. (2013) · 2013 IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.54 · Cited by 415
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Openface 2.0: facial behavior analysis toolkit
Baltrusaitis, T., Zadeh, A., Lim, Y. C., & Morency, L.-P. (2018) · 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) · doi:10.1109/fg.2018.00019 · Cited by 1,861
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion
Williams, J., Kleinegesse, S., Comanescu, R., & Radu, O. (2018) · Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML) · doi:10.18653/v1/w18-3302 · Cited by 132
Human Gaze Assisted Artificial Intelligence: A Review
Zhang, R., Saran, A., Liu, B., Zhu, Y., Guo, S., Niekum, S., Ballard, D., & Hayhoe, M. (2020) · Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/689 · Cited by 48
Metadata
- Authors
- Wood, E., Baltruaitis, T., Zhang, X., Sugano, Y., Robinson, P., & Bulling, A.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 3756-3764
- OpenAlex
- W1769933788
- Cited by
- 308
- References
- 2
- FWCI
- 9.01
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wood, E., Baltruaitis, T., Zhang, X., Sugano, Y., Robinson, P., & Bulling, A. (2015). Rendering of Eyes for Eye-Shape Registration and Gaze Estimation. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.428
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