Face Identification for an in-vehicle Surveillance System Using Near Infrared Camera

Face identification is an essential topic in surveillance system research. Surveillance systems have many unconstrained conditions, e.g., brightness, occlusion, and user state variations. In this paper, we propose a multi-SVM based face recognition method using a near-infrared camera. Our method has a face identification scenario optimized for an in-vehicle surveillance system, which comprises two steps: (i) registering a driver and (ii) recognizing whether the driver is a registered. We perform feature extraction and recognition for each facial landmark. In the case of extreme exposure to light, we convert normal face images into simulated light overexposed images for learning. Thus, face classifiers for normal and extreme illumination conditions are simultaneously generated. We also create a new face dataset and evaluate our method with both our new and PolyU NIR datasets. Experimental results show that we achieve significantly higher recognition accuracy than existing methods.