Gaze Classification on Redacted Videos
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-58174-8_23
Reference18 articles.
1. Alshaibani, A., Carrell, S., Tseng, L.H., Shin, J., Quinn, A.: Privacy-preserving face redaction using crowdsourcing. In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, vol. 8, pp. 13–22 (2020)
2. Bozkir, E., Ünal, A.B., Akgün, M., Kasneci, E., Pfeifer, N.: Privacy preserving gaze estimation using synthetic images via a randomized encoding based framework. In: ACM Symposium on Eye Tracking Research and Applications, pp. 1–5 (2020)
3. Chakrabarti, B., Haffey, A., Canzano, L., Taylor, C.P., McSorley, E.: Individual differences in responsivity to social rewards: insights from two eye-tracking tasks. PLOS ONE (2017)
4. Deng, J., Guo, J., Ververas, E., Kotsia, I., Zafeiriou, S.: Retinaface: single-shot multi-level face localisation in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5203–5212 (2020)
5. Dubey, I., et al.: Quantifying preference for social stimuli in young children using two tasks on a mobile platform. PLOS ONE 17(6) (2022)
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