Abstract—Driver fatigue cause each year a large number of road traffic accidents, this problem sparks the interest of research to move towards development of systems for prevention of this phenomenon. This article implements a face detection process as a preliminary step to monitor the state of drowsiness on vehicle's drivers. We propose an algorithm for pre-detection based on image processing and machine learning methods. A Gabor filter bank is used for facial features extraction. The dimensionality of the resulting feature space is further reduced by PCA technique and then follows a classification of Face/No Face classes using Support Vector Machine (SVM), for face detection. The simulation results on both databases namely PIE and ORL datasets show the efficiency of this approach.

Index Terms— drowsiness, car driver, face detection, gabor filter, PCA, SVM classifier

Cite: Djamel Eddine Benrachou, Brahim Boulebtateche, and Salah Bensaoula ，,,,,,,"Gabor/PCA/SVM-Based Face Detection for Driver’s Monitoring ," Journal of Automation and Control Engineering, Vol.1, No.2, pp. 115-118, June 2013. doi: 10.12720/joace.1.2.115-118