A Realistic Simulation Tool for Testing Face Recognition Systems under Real-World Conditions

Abstract

In this article, a tool for testing face recognition systems under uncontrolled conditions is proposed. The key elements of this tool are a simulator and real face and background images taken under real-world conditions with different acquisition angles. Inside the simulated environment, an observing agent, the one with the ability to recognize faces, can navigate and observe the real face images, at different distances, angles and with indoor or outdoor illumination. During the face recognition process, the agent can actively change its viewpoint and relative distance to the faces in order to improve the recognition results. The simulation tool provides all functionalities to the agent (navigation, positioning, face’s image composing under different angles, etc.), except the ones related with the recognition of faces. This tool could be of high interest for HRI applications related with the visual recognition of humans, as the ones included in the RoboCup @Home league. It allows comparing and quantifying the face recognition capabilities of service robots under exactly equal working conditions. It could be a complement to existing tests in the RoboCup @Home league. The applicability of the proposed tool is validated in the comparison of three state of the art face recognition methods.