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

摘要

Reviews, Refinements and New Ideas in Face Recognition 294 novel classification method, called the nearest feature line (NFL), for face recognition was proposed The derived FL can capture more variations of face images than the original feature points do, and it thus expands the capacity of an available face image database. However, if there are a lot of face images in the database, the recognition accuracy will reduce. Two simple but general strategies for a common face image database are compared and developed two new algorithms Nevertheless, under the different lighting conditions, the characteristic of geometry will change. In the literature However, their approach is unable to carry out in real time. On the whole, the methods of tracking objects can be categorized into three ways: match tracking, predictive tracking, and energy functions Match tracking has to detect moving objects in the entire image. Although the accuracy of match tracking is considerable, it is very time-consuming and we can not improve the performance effectively. Energy functions are often adopted in a snake model. They provide a good help for contour tracking. In general, there are two methods of predictive tracking: the Kalman filter and a particle filter. The Kalman filter has great effects when the objects move in linear paths, but it's not appropriate for the non-linear and non-Gaussian movements of objects. On the contrary, the particle filter performs well for non-linear and non-Gaussian problems. A research team completed a face tracking system which exploits a simple linear Kalman filter They used a small number of critical rectangle features selected and trained by an AdaBoost learning algorithm, and then detected the initial position, size, and incline angle of a human face correctly. Once a human face is reliably detected, they extract the colour distributions of the face and the upper body from the detected facial regions and the upper body regions for creating respective robust colour modelling by virtue of k-means clustering and multiple Gaussian models. Then fast and efficient multi-view face tracking is executed using several critical features and a simple linear Kalman filter. However, two critical problems, lighting condition change and the number of clusters in the k-means clustering, are not solved well yet. In addition to the Kalman filter, some real-time face tracking systems based on particle filtering techniques were proposed The researchers utilized a particle filter to localize human faces in image sequences. Since they have considered the hair colour information of a human head, it will keep tracking even if the person is back to the sight of a camera. In this chapter, an automatic real-time multiple faces recognition and tracking system installed on a person following robot is presented, which is inclusive of face detection, face recognition, and face tracking procedures. To identify human faces quickly and accurately, an AdaBoost algorithm is used for training a strong classifier for face detection As to face recognition, we modify the discriminative common vectors (DCVs) algorithm To raise the confidence level, the most likely person is determined by the majority voting of ten successive recognition results from a face image sequence. In the sequel, the results of recognition will be assigned into two classes: "master" and "stranger."