The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Appearance-based gaze estimation in the wild
Open accessAbstract
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440
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-citationReal-time eye gaze tracking with an unmodified commodity webcam employing a neural network
Sewell, W., & Komogortsev, O. (2010) · CHI '10 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1753846.1754048 · Cited by 136
Cited by
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
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
Kellnhofer, P., Recasens, A., Stent, S., Matusik, W., & Torralba, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00701 · Cited by 470
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Ignatov, A., Timofte, R., Chou, W., Wang, K., Wu, M., Hartley, T., & Van Gool, L. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-11021-5_19 · Cited by 347
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
- Zhang, X., Sugano, Y., Fritz, M., & Bulling, A.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 4511-4520
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2027879843
- Cited by
- 763
- References
- 5
- FWCI
- 30.38
- Type
- conference paper
- Language
- en
- Open access
- green
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