Name
Abstract | ||
|---|---|---|
•A robust fatigue detection system is constructed by introducing binocular consistency and artificial modulation into CNN.•BCNN introduces binocular consistency constraint into multi-stream network through information interaction module.•GP-BCNN incorporates dual artificial modulation into BCNN to guide the network to learn in a faster and better way.•ESM is proposed to eliminate the detected errors caused by the occluded eyes when the lateral face is detected.•GP-BCNN with ESM obtains the state-of-the-art results and has the generalization potential in general recognition tasks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patrec.2019.03.013 | Pattern Recognition Letters |
Keywords | Field | DocType |
Fatigue detection,Eye gaze pattern,Convolutional neural network,Information interaction,Artificial modulation | Computer vision,Pattern recognition,Convolutional neural network,Pattern analysis,Pupil,Modulation,Eye tracking,Artificial intelligence,Deep learning,Pattern detection,Mathematics,Integral projection | Journal |
Volume | ISSN | Citations |
123 | 0167-8655 | 4 |
PageRank | References | Authors |
0.41 | 0 | 3 |