Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering
Abstract
:1. Introduction
2. Related Work
3. Methods
3.1. The Proposed Method
3.2. Pre-Processing of the Extracted Facial Landmarks
3.3. Peak Detection
3.4. Finite State Machine
- The state machine increments from State 0 to State 1. In State 1, if the peak duration lasts for a period of less than 100 ms, it is considered an invalid blink and the state is reset to 0.
- In State 2, if the peak period lasts from 100 to 500 ms (16 frames for 30 fps video), it is considered a valid blink. The state resets to 0, and the blink is validated.
- If the state reaches 3, the eye closing period is considered to be more than 500 ms, so an invalid blink is concluded.
3.5. Eyeblink Datasets
- ZJU: This database [12] consists of 80 videos of 20 individuals. Each individual has 4 clips: frontal view, upward view, with glasses, and without glasses. Each clip lasts a few seconds and is 30 fps with a resolution of 320 × 240. There is no facial expression and almost no head movements. This dataset has different numbers of ground truth eye blinks reported, as shown in Table 1. A ground truth blink is defined by its beginning frame, peak frame, and ending frame.
- Eyeblink8: This dataset is more challenging as it contains facial expressions, head movements, and looking down on a keyboard. This dataset consists of 408 blinks on 70,992 video frames, as annotated by [21] with a video resolution of 640 × 480 captured at 30 fps with an average length from 5000 to 11,000 frames.
4. Results
4.1. Eye Blink Detection
4.2. Eye Blink Statistics
5. Conclusions
Author Contributions
Conflicts of Interest
Abbreviations
fwhm | full width at high maximum |
LBP | local binary patterns |
WGD | weighted gradient descriptor |
ASM | active shape models |
SG | Savitzky–Golay |
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Reference | Dataset | Precision | Recall |
---|---|---|---|
[21] | ZJU | 100% | 98.08% |
[5] | ZJU | 94.4% | 91.7% |
[13] | ZJU | 91% | 73.1% |
Proposed method | ZJU | 100% | 98.01% |
[21] | Talking face | 95% | 93.44% |
[5] | Talking face | 83.3% | 91.2% |
[13] | Talking face | 92.2% | 96.7% |
Proposed method | Talking face | 98.38% | 98.38% |
[21] | Eyeblink8 | 94.69% | 91.91% |
Proposed method | Eyeblink8 | 96.65% | 98.78% |
Dataset | Video | GT | DB | RGT | RD | Average Duration (ms) |
---|---|---|---|---|---|---|
Eyeblink8 | 1 | 38 | 43 | 4.4 | 5.1 | 395 |
2 | 88 | 89 | 14.3 | 14.4 | 297 | |
3 | 65 | 67 | 12.7 | 13.1 | 222 | |
4 | 31 | 31 | 10.3 | 10.3 | 245 | |
8 | 30 | 34 | 5.3 | 6.1 | 190 | |
9 | 41 | 41 | 16.2 | 16.2 | 224 | |
10 | 72 | 72 | 14.2 | 14.2 | 177 | |
11 | 43 | 44 | 17.6 | 18.1 | 254 | |
Talkingface | 1 | 61 | 61 | 24.7 | 24.7 | 184 |
ZJU | 261 | 256 | 61 | 48.96 | 48.03 | 190 |
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Al-gawwam, S.; Benaissa, M. Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering. Information 2018, 9, 93. https://doi.org/10.3390/info9040093
Al-gawwam S, Benaissa M. Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering. Information. 2018; 9(4):93. https://doi.org/10.3390/info9040093
Chicago/Turabian StyleAl-gawwam, Sarmad, and Mohammed Benaissa. 2018. "Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering" Information 9, no. 4: 93. https://doi.org/10.3390/info9040093
APA StyleAl-gawwam, S., & Benaissa, M. (2018). Robust Eye Blink Detection Based on Eye Landmarks and Savitzky–Golay Filtering. Information, 9(4), 93. https://doi.org/10.3390/info9040093