An Adaptive Face Tracker with Application in Yawning Detection

Sensors

卷:20

Jacob Scharcanski¹  Aasim Khurshid² 

[1] Instituto de Informatica, UFRGS, Porto Alegre 9500, Brazil;

[2] Sidia Instituto de Ciencia e tecnologia, Amazonas, Manaus 69055-035, Brazil;

关键词: face tracking;    error prediction;    features resyncing;    online learning;    incremental pca;    yawning detection;    feature extraction for emotion analysis;   

DOI  :  10.3390/s20051494

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown