Robust Open-Set Face Recognition for Small-Scale Convenience Applications
In this paper, a robust real-world video based open-set face recognition system is presented. This system is designed for general small-scale convenience applications, which can be used for providing customized services. In the developed prototype, the system identifies a person in question and conveys customized information according to the identity. Since it does not require any cooperation of the users, the robustness of the system can be easily affected by the confounding factors. To overcome the pose problem, we generated frontal view faces with a tracked 2D face model. We also employed a distance metric to assess the quality of face model tracking. A local appearance-based face representation was used to make the system robust against local appearance variations. We evaluated the system’s performance on a face database which was collected in front of an office. The experimental results on this database show that the developed system is able to operate robustly under real-world conditions.
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