A survey of skin-color modeling and detection methods
Pattern Recognition
Saliency model-based face segmentation and tracking in head-and-shoulder video sequences
Journal of Visual Communication and Image Representation
An adaptive multiple model approach for fast content-based skin detection in on-line videos
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Automatic segmentation of characteristic areas of the human head on thermographic images
Machine Graphics & Vision International Journal
Multi-stage combination of geometric and colorimetric detectors for eyes localization
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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Abstract: We present a multivariate statistical model to represent the human skin color. In our approach, there are no limitations regarding if the person is white or black, once the model is able to learn automatically the ethny of the people involved. We propose to model the skin color in the chromatic subspace, which is by default normalized with respect to illumination. First, skin samples from both white and black people are collected. These samples are then used to estimate a parametric statistical model, which consists of a mixture of gaussian probability density functions (pdf's). Estimation is performed by a learning process based on the expectation-maximization (EM) algorithm. In the following, experiments are carried out and receiving operating characteristics (ROC curves) are obtained to analyse the performance of the estimated model and compare it to outcomes of models that use a single gaussian density. Finally, conclusions are presented and future work is outlined.