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Speedy and reliable face location is a key for monitoring driver fatigue driving condition using machine vision methods. According to the disadvantage of face location methods based on single feature in accuracy and reliability at present, first, an improved face location method based on Haar-like feature and Adaboost is used to detect the possibly existing face region in the whole image, then the detected region is extended properly and a face location method based on skin color feature in normalized rgb and HSV color spaces is used to locate the face again in the extended area, finally, fusion location of driver face region is achieved by the defined area coincidence degree and prior knowledge of human face. Experiments carried out in various road environments demonstrate the validity of the fusion method proposed.

Exact and fast driver face detection is a key for recognising whether drivers are fatigue or not by detecting ficial organs while driving using machine vision technology. Aiming at the limitation of driver face detection algorithm based on single feature in detection precision and reliability, a novel fusion algorithm of driver face detection is proposed. Firstly, an improved face detection algorithm based on Haar-like feature is used to detect the possibly existing initial face region in the whole image, then the initial face region detected is extended properly and a face detection algorithm based on skin color feature in rgb space is used to locate the face region again in the extended area, finally, fusion detection of driver face region is achieved by the defined area coincidence degree and geometric prior knowledge of human face. Experiments carried out in various complicated road environments show the algorithm proposed is of strong robustness on lighting changes, driver head rotation, and having glasses, etc., while at detection precision and reliability, it offers a noticeable enhancement compared with the single feature based algorithms.