Applied multivariate statistical analysis
Applied multivariate statistical analysis
The nature of statistical learning theory
The nature of statistical learning theory
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Robust Real-Time Face Detection
International Journal of Computer Vision
Tracking a detected face with dynamic programming
Image and Vision Computing
An effective method for detecting facial features and face in human-robot interaction
Information Sciences: an International Journal
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In this paper, we propose a real-time face tracking system using adaptive face detector and the Kalman filter. Basically, the features used for face detection are five types of simple Haar-like features. To only extract the more significant features from these features, we employ principal component analysis (PCA). The extracted features are used for a learning vector of the support vector machine (SVM), which classifies the faces and non-faces. The face detector locates faces from the face candidates separated from the background by using real-time updated skin color information. We trace the moving faces with the Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. In this experiment, the proposed system showed an average tracking rate of 97.3% and a frame rate of 23.5 frames per s, which can be adapted into a real-time tracking system.