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. | Journal of Automation and Control Engineering, Vol. 1, No. 2, June 2013 Gabor/PCA/SVM-Based Face Detection for Driver’s Monitoring Djamel Eddine Benrachou, Brahim Boulebtateche, and Salah Bensaoula University Badji Mokhtar, Department of electronic, Annaba, Algeria , {bbouleb, bensaoula_salah}@ 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. Dimensionality reduction is adopted by PCA technique to create low dimensional features vectors for more convenient processing. SVM is used to extract relevant information from this low dimensional training data in order to construct a robust specific classifier. This method has been tested on two available AT&T (ORL) and (PIE) Databases of human faces. The statistical evaluation is presented for two different databases using both SVM's kernels namely linear and Gaussian kernels, implemented separately in order to detect the presence of a face or not. A. Proposed Algorithm The use of non-intrusive drowsiness detection methods requires several processing modules. In the proposed approach a first step of extracting essential features of the face detection is performed by applying the Gabor's representation on the image database. The advantage of this .
