An adaptive skin model and its application to objectionable image filtering
Proceedings of the 12th annual ACM international conference on Multimedia
Interacting with Digital Signage Using Hand Gestures
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Tracking multiple people with recovery from partial and total occlusion
Pattern Recognition
Object tracking via uncertainty minimization
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Adaptive learning of an accurate skin-color model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Computer Methods and Programs in Biomedicine
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Skin color offers a strong cue for efficient localization and tracking of human body parts in video sequences for vision-based human-computer interaction. Color-based target localization could be achieved by analyzing segmented skin color regions. However, one of the challenges of color-based target tracking is that color distributions would change in different lighting conditions such that fixed color models would be inadequate to capture nonstationary color distributions over time. Meanwhile, using a fixed skin color model trained by the data of a specific person would probably not work well for other people. Although some work has been done on adaptive color models, this problem still needs further studies. We present our investigation of color-based image segmentation and nonstationary color-based target tracking, by studying two different representations for color distributions. We propose the structure adaptive self-organizing map (SASOM) neural network that serves as a new color model. Our experiments show that such a representation is powerful for efficient image segmentation. Then, we formulate the nonstationary color tracking problem as a model transduction problem, the solution of which offers a way to adapt and transduce color classifiers in nonstationary color distributions. To fulfill model transduction, we propose two algorithms, the SASOM transduction and the discriminant expectation-maximization (EM), based on the SASOM color model and the Gaussian mixture color model, respectively. Our extensive experiments on the task of real-time face/hand localization show that these two algorithms can successfully handle some difficulties in nonstationary color tracking. We also implemented a real-time face/hand localization system based on such algorithms for vision-based human-computer interaction.