An Adaptive Face Tracker with Application in Yawning Detection
In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not misse...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/5/1494 |
| _version_ | 1798034508484706304 |
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| author | Aasim Khurshid Jacob Scharcanski |
| author_facet | Aasim Khurshid Jacob Scharcanski |
| author_sort | Aasim Khurshid |
| collection | DOAJ |
| description | In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection. |
| first_indexed | 2024-04-11T20:45:08Z |
| format | Article |
| id | doaj.art-42467f1abbda41208cfe8533cfcbcddc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:45:08Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-42467f1abbda41208cfe8533cfcbcddc2022-12-22T04:04:03ZengMDPI AGSensors1424-82202020-03-01205149410.3390/s20051494s20051494An Adaptive Face Tracker with Application in Yawning DetectionAasim Khurshid0Jacob Scharcanski1Sidia Instituto de Ciencia e tecnologia, Amazonas, Manaus 69055-035, BrazilInstituto de Informatica, UFRGS, Porto Alegre 9500, BrazilIn this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection.https://www.mdpi.com/1424-8220/20/5/1494face trackingerror predictionfeatures resyncingonline learningincremental pcayawning detectionfeature extraction for emotion analysis |
| spellingShingle | Aasim Khurshid Jacob Scharcanski An Adaptive Face Tracker with Application in Yawning Detection Sensors face tracking error prediction features resyncing online learning incremental pca yawning detection feature extraction for emotion analysis |
| title | An Adaptive Face Tracker with Application in Yawning Detection |
| title_full | An Adaptive Face Tracker with Application in Yawning Detection |
| title_fullStr | An Adaptive Face Tracker with Application in Yawning Detection |
| title_full_unstemmed | An Adaptive Face Tracker with Application in Yawning Detection |
| title_short | An Adaptive Face Tracker with Application in Yawning Detection |
| title_sort | adaptive face tracker with application in yawning detection |
| topic | face tracking error prediction features resyncing online learning incremental pca yawning detection feature extraction for emotion analysis |
| url | https://www.mdpi.com/1424-8220/20/5/1494 |
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