Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Machine Vision and Applications
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
Face detection method based on skin color segmentation and facial component localization
CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
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For face detection, while pose and illumination significantly change the global facial appearance, components of a face are less affected by these changes. Component detectors can accurately locate facial components, and component-based approaches can be used to check whether the geometric locations of the components comply with a face. This paper proposes a face detection and verification method using component-based online learning, which is based on using unsupervised clustering to find a set of templates specific to faces consisting of face component and their relations. The main difference from previously reported component-based face detection methods is the use of online learning, which is ideal for highly repetitive tasks. This results in faster and more accurate face detection, because system performance improves with continued use. Further, uncertainty is added by calculating the standard deviation of face components and their relations. A component-based method with uncertainty provides flexibility to allow variability to describe an object in appearance and geometry.