IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical color models with application to skin detection
International Journal of Computer Vision
Coarse to Fine Face Detection Based on Skin Color Adaption
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Soft Computing and Tools of Intelligent Systems Design: Theory and Applications
Fast and Accurate Skin Segmentation in Color Images
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixture Clustering Using Multidimensional Histograms for Skin Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face Detection Using Improved LBP under Bayesian Framework
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Face detection with boosted Gaussian features
Pattern Recognition
Face Detection in Color Images Using AdaBoost Algorithm Based on Skin Color Information
WKDD '08 Proceedings of the First International Workshop on Knowledge Discovery and Data Mining
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first estimated using the Estimation Maximization (EM) algorithm. Then, feature based classification differentiates face related pixels from other skin regions and objects with close intensity values. A novel approach for classifying faces using a structure of cooperating neural networks, for which learning parameters are generated using Adaboost learning method is proposed. In addition, a new approach is also proposed for training the neural network with reduced space Haar-like features instead of working with image pixels themselves. Principle component analysis was used to find the aspects of features that are crucial for detection. The features dimensionality was reduced by nearly 90 percent, hence improving radically the training time. When adequate parameters are chosen, the system yields face detection characteristics that outperform the best existing algorithms (such as the one proposed by Viola and Jones) in terms of accuracy. Finally, the parameters of the mixture of Gaussians model are updated based on the results of the classification testing results to increase its robustness against illuminations and other external environmental changes, as well as reducing even more the search space.