Catching moving objects with snakes for motion tracking
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Face Detection Using Integral Projection Models
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Segmentation and Tracking of Faces in Color Images
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A model-based gaze tracking system
IJSIS '96 Proceedings of the 1996 IEEE International Joint Symposia on Intelligence and Systems
Accurate, Real-Time, Unadorned Lip Tracking
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A real-time face tracker for color video
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Tierra Inhospita: exploring a virtual world with your face
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
Human face processing with 1.5D models
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Lips shape extraction via active shape model and local binary pattern
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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Integral projections can be used, by themselves, to accurately track human faces in video sequences. Using projections, the tracking problem is effectively separated into the vertical, horizontal and rotational dimensions. Each of these parts is solved, basically, through the alignment of a projection signal -a one-dimensional pattern- with a projection model. The effect of this separation is an important improvement in feature location accuracy and computational efficiency. A comparison has been done with respect to the CamShift algorithm. Our experiments have also shown a high robustness of the method to 3D pose, facial expression, lighting conditions, partial occlusion, and facial features.