Gabor/PCA/SVM-Based Face Detection for Driver’s Monitoring

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. 