A virtual environment for realistic testing and training of face detection and recognition systems

In this article, a virtual environment for realistic testing and training of face detection and 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 conditions. 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 virtual environment provides all behaviors 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 in HRI applications related with the visual recognition of humans. It allows comparing and quantifying the face recognition capabilities of service robots, and in general intelligent machines, under exactly equal working conditions. The applicability of the proposed tool is validated in the comparison of state of the art face detection and recognition methods.