Multi-feature driver face detection based on area coincidence degree and prior knowledge

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.