Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Multi-Modal System for Locating Heads and Faces
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Face Detection for Visual Surveillance
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Document image segmentation using wavelet scale-space features
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
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In this paper, a real-time face tracking and recognition system based on particle filtering and AdaBoosting techniques is presented. Regarding the face tracking, we develop an effective particle filter to locate faces in image sequences. Since we have considered the hair color information of a human head, the particle filter will keep tracking even if the person is back to the line of sight of a camera. We further adopt both the motion and color cues as the features to make the influence of the background as low as possible. A new fashion of classification architecture trained with an AdaBoost algorithm is also proposed to achieve face recognition rapidly. Compared to other machine learning schemes, the AdaBoost algorithm can update training samples to deal with comprehensive circumstances, but it need not spend much computational cost. Experimental results reveal that the face tracking rate is more than 97% in general situations and 89% when the face suffering from temporal occlusion. As for the face recognition, the accuracy rate is more than 90%; besides this, the efficiency of system execution is very satisfactory, which reaches 20 frames per second at least.