Face Tracking Based on Haar-like Features and Eigenfaces

This paper describes an algorithm for human tracking using vision sensing, specially designed for a human machine interface of a mobile robotic platforms or autonomous vehicles. The solution presents a clear improvement on a tracking algorithm achieved by using a machine learning approach for visual object detection and recognition for data association. The system is capable of processing images rapidly and achieving high detection and recognition rates. This framework is demonstrated on the task of human-robot interaction. There are three key parts on this framework. The first is the person’s face detection used as input for the second stage which is the recognition of the face of the person interacting with the robot, and the third one is the tracking of this face along the time. The detection technique is based on Haar-like features, whereas eigenimages and PCA are used in the recognition stage of the system. The tracking algorithm uses a Kalman filter to estimate position and scale of the person’s face in the image. The data association is accelerated by using a subwindow whose dimensions are automatically defined from the covariance matrix of the estimate. Used in realtime human-robot interaction applications, the system is able to detect, recognise and track faces at about 16 frames per second in a conventional 1GHz PentiumIII laptop.