Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Robust Real-Time Face Detection
International Journal of Computer Vision
Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Eye Detection and Its Validation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
A Novel Eye Location Algorithm based on Radial Symmetry Transform
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Precise eye localization using HOG descriptors
Machine Vision and Applications
Face Recognition with Integrating Multiple Cues
Journal of Signal Processing Systems
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Automatic detection and precise localization of human eye centers are the essential processes in photo related multimedia applications. Since eye center points are used as reference base points for further intelligent processing, precise eye center localization is very important. In face recognition the accuracy of localization of eye centers directly influences the identification accuracy. A multiple stage approach with multiple cues for detection and precise localization of eye centers is presented in this paper. Multiple scopes searching strategy is used for correctly extracting eye patch images from the background. Dedicated gradient based features and curvelet based features are constructed and used for comprehensively revealing the intensity distribution characteristics and the edge based texture around eye centers. A rebuilt score calculation mechanism is proposed and the rebuilt scores are used as a specific measurement index reflecting the matching accuracy. The final localizations of eye centers are determined with integrating the gradient based scores and curvelet based scores. The experiment results testing on public face datasets show that the localization accuracy of proposed approach outperforms the accuracy with other state of the art methods.