Abstract
Human concentration state detection using the eye movement information is now a popular research topic in computer vision, especially the detection of driver fatigue and advertising analysis. In this paper we analyze eye movement styles on a person’s concentration state through watching different video clips. We propose a novel method including the fusion features of eye event data and raw eye movement to detect attention. Firstly, we use the logistic regression algorithm to conduct the new feature by eye movement event data, and use wavelet and approximate entropy algorithm to conduct the new feature by raw eye movement data. Secondly, we train attention detection model using these new merged features. In order to avoid the problem caused by insufficient samples, crossing method is used to train the model to ensure its accuracy. Our model achieves a satisfying 95.25% accuracy.
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Appendix
Appendix
Feature Symbol
Feature | Blinks count | Bduration | Max duration | Fixation count |
Symbol | BC | BD | BDMAX | FC |
Feature | Fduration | Fduration avg | Fdeviation | Slength |
Symbol | FD | FDA | FDe | SL |
Feature | Scount | Sduration | Samplitude | Svelocity |
Symbol | SC | SD | SA | SV |
Feature | LR coef | Relief weight | DT weight | Eye position |
Symbol | LRFM | ReFM | DTFM | EPA |
Feature | Gaze position | Pupil position | Pupil diameter | |
Symbol | GPA | PDA | PPA |
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Liu, B., Jiang, P., Wang, F., Zhang, X., Hao, H., Bai, S. (2018). Attention Detection by Learning Hierarchy Feature Fusion on Eye Movement. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_53
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DOI: https://doi.org/10.1007/978-3-319-97909-0_53
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