Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Pattern Recognition Letters
A flexible image database system for content-based retrieval
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
Computer Vision
Edge detector evaluation using empirical ROC curves
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Statistical color models with application to skin detection
International Journal of Computer Vision
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Skin-Color Modeling and Adaptation
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
LAFTER: Lips and Face Real-Time Tracker
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
User Localisation for Visually-Based Human-Machine-Interaction
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Comparison of Five Color Models in Skin Pixel Classification
RATFG-RTS '99 Proceedings of the International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems
Does Colorspace Transformation Make Any Difference on Skin Detection?
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Effect of colorspace transformation, the illuminance component, and color modeling on skin detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Skin detection using neighborhood information
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
Task-based evaluation of skin detection for communication and perceptual interfaces
Journal of Visual Communication and Image Representation
An incremental PCA-HOG descriptor for robust visual hand tracking
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Color based skin classification
Pattern Recognition Letters
Efficient skin detection under severe illumination changes and shadows
ICIRA'11 Proceedings of the 4th international conference on Intelligent Robotics and Applications - Volume Part II
Skin detection by dual maximization of detectors agreement for video monitoring
Pattern Recognition Letters
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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Skin detection is an important indicator of human presence and actions in many domains, including interaction, interfaces and security. It is commonly performed in three steps: transforming the pixel color to a non-RGB colorspace, dropping the illuminance component of skin color, and classifying by modeling the skin color distribution. In this paper, we evaluate the effect of these three steps on the skin detection performance. The importance of this study is a new comprehensive colorspace and color modeling testing methodology that would allow for making the best choices for skin detection. Combinations of nine colorspaces, the presence or the absence of the illuminance component, and the two color modeling approaches are compared for different settings (indoor or outdoor) and modeling parameters (the histogram size). The performance is measured by using a receiver operating characteristic (ROC) curve on a large dataset of 845 images (consisting more than 18.6 million pixels) with manual ground truth. The results reveal that (1) colorspace transformations can improve performance in certain instances, (2) the absence of the illuminance component decreases performance, and (3) skin color modeling has a greater impact than colorspace transformation. We found that the best performance was obtained on indoor images by transforming the pixel color to the HSI or SCT colorspaces, keeping the illuminance component, and modeling the color with the histogram approach using a larger size distribution.