Efficient illumination normalization of facial images
Pattern Recognition Letters
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Efficient Design of Advanced Correlation Filters for Robust Distortion-Tolerant Face Recognition
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Robust Real-Time Face Detection
International Journal of Computer Vision
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
Total Variation Models for Variable Lighting Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
A hybrid classifier for precise and robust eye detection
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
A Learning-based Eye Detector Coupled with Eye Candidate Filtering and PCA Features
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Computer Vision and Image Understanding
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ADNTIIC'10 Proceedings of the First international conference on Advances in new technologies, interactive interfaces, and communicability
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Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A new machine learning approach and several correlation filter approaches, including a new adaptive variant, are compared. We present experimental results on a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system. The results of our experiments show that our new detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem.