Face recognition in vehicles with near infrared frame differencing

Variations in illumination negatively impacts the performance of most face recognition systems. This is substantially exacerbated when the illumination on a face exhibits strong shadows or other anomalies. This paper describes a system of practical technologies to implement an illumination robust, consumer grade biometric system based on face recognition to be used in the automotive market. It addresses the challenging outdoor environments in which driver identification is expected to operate. The point of this research is to investigate practical face recognition used for identity management in order to minimize algorithmic complexity while making the system robust to ambient illumination changes. First, we present a frame differencing method with an active near-infrared illumination control that produces images independent of the ambient illumination. Second, end-to-end face recognition system is presented including motion detection, face detection and face recognition modules. And it is shown that the frame differencing method makes the modules more robust to the ambient illumination variation. Vehicular application videos were taken in extremely challenging outdoor illumination and shadowing conditions and used to test each module. Finally, extensive test results of vehicular scenario are provided to evaluate the end-to-end systems.