Real-time Multi-Face Recognition and Tracking Techniques Used for the Interaction between Humans and Robots

Abstract

The technology of biometric recognition systems for personal identification commonly manipulate the input data acquired from irises, voiceprints, fingerprints, signatures, human faces, and so on. The recognition of irises, voiceprints, fingerprints, and signatures belongs to the passive methods that require the camera with a high resolution or capture people‘s biometric information at a short range. These methods are not suitable for our person following robot to be developed, because they cannot provide convenience for users. Face recognition belongs to one of the active methods that users need keep away from a camera at a certain distance only. In this chapter, face recognition is regarded as a kind of human computer interfaces (HCIs) that are applied to the interaction between humans and robots. Thus, we attempt to develop an automatic real-time multiple faces recognition and tracking system equipped on the robot that can detect human faces and confirm a target in an image sequence captured from a PTZ camera, and keep tracking the target that has been identified as a master or stranger, then employ a laser range finder to measure a proper distance between target’s owner and the robot. Generally, three main procedures: face detection, face recognition, and face tracking are implemented on such a system. In literature, a large number of face localization techniques had been proposed. According to the literature (Hjelmas & Low, 2001), the methods for face detection can be basically grouped into feature-based and image-based approaches. The development of the feature-based approaches can be further divided into three areas: active shape models, feature analysis, and low-level analysis for edges, grey-levels, skin colours, motions, and so forth. On the other hand, the image-based approaches can be categorized into linear subspace methods, neural networks, and statistical approaches. The development of face recognition is more and more advanced in the past twenty years (Zhao et al., 2003). Each system has its own solution. At present, all researches about face recognition take the features that are robust enough to represent different human faces. A