Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection in Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking the human arm using constraint fusion and multiple-cue localization
Machine Vision and Applications
Stochastic Human Segmentation from a Static Camera
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Tracking Humans from a Moving Platform
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Principal Axis-Based Correspondence between Multiple Cameras for People Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Template-Based hand pose recognition using multiple cues
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Cue combination for robust real-time multiple face detection at different resolutions
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Motion Primitives and Probabilistic Edit Distance for Action Recognition
Gesture-Based Human-Computer Interaction and Simulation
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Many potential applications exist where a fast and robust detection of human faces is required. Different cues can be used for this purpose. Since each cue has its own pros and cons we, in this paper, suggest to combine several complimentary cues in order to gain more robustness in face detection. Concretely, we apply skin-color, shape, and texture to build a robust detector. We define the face detection problem in a state-space spanned by position, scale, and rotation. The statespace is searched using a Particle Filter where 80% of the particles are predicted from the past frame, 10% are chosen randomly and 10% are from a texture-based detector. The likelihood of each selected particle is evaluated using the skin-color and shape cues. We evaluate the different cues separately as well as in combination. An improvement in both detection rates and false positives is obtained when combining them.