Characterization and detection of noise in clustering
Pattern Recognition Letters
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Differential Feature Distribution Maps for Image Segmentation and Region Queries in Image Databases
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A survey of skin-color modeling and detection methods
Pattern Recognition
A combined skin model and feature approach for tracking of human faces
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
A skin detection approach based on the Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
Detecting Facial Expressions for Monitoring Patterns of Emotional Behavior
International Journal of Monitoring and Surveillance Technologies Research
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In this paper we present a skin color approach for fast and accurate face detection which combines skin color learning and image segmentation. This approach starts from a coarse segmentation which provides regions of homogeneous statistical color distribution. Some regions represent parts of human skin and are selected by minimizing an error between the color distribution of each region and the output of a compression decompression neural network, which learns skin color distribution for several populations of different ethnicity. This ANN is used to find a collection of skin regions which are used to estimate the new parameters of the Gaussian models using a 2-means fuzzy clustering in order to adapt these parameters to the context of the input image. A Bayesian frameworkis used to perform a finer classification and makes the skin and face detection process invariant to scale and lighting conditions. Finally, a face shape based model is used to validate or not the face hypothesis on each skin region.