An Adaptive Snake Based Shadow Segmentation for Robust Driver Fatigue Detection: A 3D Facial Feature Based Photometric Stereo Perspective

Abstract

Fatigue detection has many applications, one of them is intelligent transportation for accident prevention. It is challenging to detect driver fatigue adopting photometric stereo and 3D fatigue-related facial action unit identification. During 3D reconstruction shadows are encountered, causing reconstruction error, therefore, for accurate fatigue detection shadow removal is crucial. This study presents a novel snake based approach for shadow handling in photometric stereo. Seven light sources were employed and region visibility of the light source for each region of interest (ROI) was deduced. Surface normal estimation enhancement was achieved via snake model based shadow labeling. Discrepancies measured among shadowed regions and ROIs were recorded. Light sources having high intersection with the shadow map were excluded from the reconstruction process at pixel level. This results in an automatic driver fatigue related action unit detection employing uncalibrated Lambertian surface under shadow conditions. The developed method was tested and compared with other established driver fatigue detection methods and higher performance in terms of accuracy ( i.e. 97.85%) was achieved. The developed method proves to be well suited for driver fatigue detection under uncalibrated conditions.