RT-BENE: A Dataset and Baselines for Real-Time Blink Estimation in Natural Environments

IEEE International Conference on Computer Vision Workshops 2019 β€’ KΓ©vin Cortacero β€’ Tobias Fischer β€’ Yiannis Demiris

In recent years gaze estimation methods have made substantial progress, driven by the numerous application areas including human-robot interaction, visual attention estimation and foveated rendering for virtual reality headsets. However, many gaze estimation methods typically assume that the subject's eyes are open; for closed eyes, these methods provide irregular gaze estimates... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Blink estimation Eyeblink8 DenseNet 121 Ensemble F1 0.976 # 1
Blink estimation Researcher's Night DenseNet 121 Ensemble F1 0.913 # 1
Blink estimation RT-BENE DenseNet 121 Ensemble F1 0.721 # 1

Methods used in the Paper


METHOD TYPE
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