Applied multivariate statistical analysis
Applied multivariate statistical analysis
The nature of statistical learning theory
The nature of statistical learning theory
X Vision: a portable substrate for real-time vision applications
Computer Vision and Image Understanding
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
IEEE Transactions on Image Processing
Robust classification of face and head gestures in video
Image and Vision Computing
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We present a robust face tracking system from the sequence of video images based on effective detector and Kalman filter. To construct the effective face detector, we extract the face features using the five types of simple Haar-like features. Extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. We trace the moving face with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. To make a real-time tracking system, we reduce processing time by adjusting the frequency of face detection. In this experiment, the proposed system showed an average tracking rate of 95.5% and processed at 15 frames per second. This means the system is robust enough to track faces in real-time.