Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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 hybrid classifier for precise and robust eye detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Multi-view face and eye detection using discriminant features
Computer Vision and Image Understanding
Eye localization in low and standard definition content with application to face matching
Computer Vision and Image Understanding
Computer Vision and Image Understanding - Special issue on eye detection and tracking
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present in this paper a precise eye detection method using Discriminating Histograms of Oriented Gradients (DHOG) features. The DHOG feature extraction starts with a Principal Component Analysis (PCA) followed by a whitening transformation on the standard HOG feature space. A discriminant analysis is then performed on the reduced feature space. A set of basis vectors, based on the novel definition of the within-class and between-class scatter vectors and a new criterion vector, is defined through this analysis. The DHOG features are derived in the subspace spanned by these basis vectors. Experiments on Face Recognition Grand Challenge (FRGC) show that (i) DHOG features enhance the discriminating power of HOG features and (ii) our eye detection method outperforms existing methods.