Face recognition for vehicle personalization with near infrared frame differencing

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. Most current face recognition systems are compromised in accuracy by ambient illumination changes. Outdoor applications including vehicle personalization pose an especially challenging environment for face recognition. 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, a frame differencing method is presented with an active near-infrared illumination control that produces images independent of the ambient illumination. Second, an end-to-end face recognition system is presented including foreground/background segmentation, motion detection, face detection, motion interpolation, pose clustering and face recognition modules. 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
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