An Improved Face Recognition System for Service Robot Using Stereo Vision

Abstract

Service robot is an emerging technology in robot vision, and demand from household and industry will be increased significantly in the future. General vision-based service robot should recognizes people and obstacles in dynamic environment and accomplishes a specific task given by a user. The ability to face recognition and natural interaction with a user are the important factors for developing service robots. Since tracking of a human face and face recognition are an essential function for a service robot, many researcher have developed face-tracking mechanism for the robot (Yang M., 2002) and face recognition system for service robot( Budiharto, W., 2010). The objective of this chapter is to propose an improved face recognition system using PCA(Principal Component Analysis) and implemented to a service robot in dynamic environment using stereo vision. The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals (Adini et al., 1997). Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, we have improved the training images by generate random value for varying the intensity of the face images. We proposed an architecture of service robot and database for face recognition system. A navigation system for this service robot and depth estimation using stereo vision for measuring distance of moving obstacles are introduced. The obstacle avoidance problem is formulated using decision theory, prior and posterior distribution and loss function to determine an optimal response based on inaccurate sensor data. Based on experiments, by using 3 images per person with 3 poses (frontal, left and right) and giving training images with varying illumination, it improves the success rate for recognition. Our proposed method very fast and successfully implemented to service robot called Srikandi III in our laboratory. This chapter is organized as follows. Improved method and a framework for face recognition system is introduced in section 2. In section 3, the system for face detection and depth estimation for distance measurement of moving obstacles are introduced. Section 4, a detailed implementation of improved face recognition for service robot using stereo vision is presented. Finally, discussions and future work are drawn in section 5.