Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Component-based robust face detection using AdaBoost and decision tree
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multi-template ASM Method for Feature Points Detection of Facial Image with Diverse Expressions
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Robust precise eye location under probabilistic framework
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Extension of cascaded simple feature based face detection to facial expression recognition
Pattern Recognition Letters
Real-Time Face Verification for Mobile Platforms
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Realtime training on mobile devices for face recognition applications
Pattern Recognition
Incremental face recognition for large-scale social network services
Pattern Recognition
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We describe a novel eye detection method that is robust to the obstacles such as surrounding illumination, hair, and eye glasses. The obstacles above a face image are constraints to detect eye position. These constraints affect the performance of the face applications such as face recognition, gaze tracking, and video indexing systems. To overcome this problem, the proposed method for eye detection consists of three steps. First, the self quotient images are applied to the face images by rectifying illumination. Then, unnecessary pixels for eye detection are removed by using the symmetry object filter. Next, the eye candidates are extracted by using the gradient descent which is a simple and a fast computing method. Finally, the classifier, which has trained by using AdaBoost algorithm, selects the eyes from all of the eye candidates. The usefulness of the proposed method has been demonstrated in an embedded system with the eye detection performance.