Name
Title | ||
|---|---|---|
Drowsiness Estimation from Low-Frame-Rate Facial Videos using Eyelid Variability Features. |
Abstract | ||
|---|---|---|
This paper proposes a method of estimating drowsiness from low-frame-rate facial videos by using eyelid variability features. Since eyelid variability involves slow motions, drowsiness can be estimated more accurately with these features at low frame rate than the conventional blink-related features, in which movements may be made in only some hundred milliseconds per blink. The correlation between the ground-truth drowsiness labels and the estimated drowsiness values is compared through facial videos with frame rates ranging from 3 to 30 frames per second (fps). With conventional blink-related features, the correlation drops from 0.59 (at 30 fps) to 0.28 (at 3 fps), while, with the proposed eyelid variability features, the correlation remains nearly constant, from 0.55 (at 30 fps) to 0.53 (at 3 fps). This characteristic makes it useful for drowsiness estimation with a low computational cost. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8513470 | EMBC |
Field | DocType | Volume |
Eyelid,Computer vision,Computer science,Feature extraction,Ranging,Frame rate,Artificial intelligence | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masanori Tsujikawa | 1 | 1 | 3.40 |
| Yoshifumi Onishi | 2 | 0 | 0.34 |
| Yukihiro Kiuchi | 3 | 0 | 0.34 |
| Toshinobu Ogatsu | 4 | 0 | 0.34 |
| Atsushi Nishino | 5 | 0 | 1.01 |
| Satoshi Hashimoto | 6 | 9 | 1.68 |