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Feature extraction from faces using deformable templates
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The FERET Evaluation Methodology for Face-Recognition Algorithms
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Real-time eye detection and tracking under various light conditions
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
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Direct Gray-Scale Extraction of Features for Character Recognition
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
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CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Editorial: special issue: eye detection and tracking
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Computer Vision and Image Understanding - Special issue on eye detection and tracking
Detecting and Tracking Eyes Through Dynamic Terrain Feature Matching
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
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Eye/gaze tracking in web, image and video documents
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Learning a restricted Bayesian network for object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Visual routines for eye location using learning and evolution
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ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Computer Vision and Image Understanding
Reshaping 3D facial scans for facial appearance modeling and 3D facial expression analysis
Image and Vision Computing
Hybrid method based on topography for robust detection of iris center and eye corners
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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Automatic eye detection and tracking is an important component for advanced human-computer interface design. Accurate eye localization can help develop a successful system for face recognition and emotion identification. In this article, we propose a novel approach to detect and track eyes using geometric surface features on topographic manifold of eye images. First, in the joint spatial-intensity domain, a facial image is treated as a 3D terrain surface or image topographic manifold. In particular, eye regions exhibit certain intrinsic geometric traits on this topographic manifold, namely, the pit-labeled center and hillside-like surround regions. Applying a terrain classification procedure on the topographic manifold of facial images, each location of the manifold can be labeled to generate a terrain map. We use the distribution of terrain labels to represent the eye terrain pattern. The Bhattacharyya affinity is employed to measure the distribution similarity between two topographic manifolds. Based on the Bhattacharyya kernel, a support vector machine is applied for selecting proper eye pairs from the pit-labeled candidates. Second, given detected eyes on the first frame of a video sequence, a mutual-information-based fitting function is defined to describe the similarity between two terrain surfaces of neighboring frames. By optimizing the fitting function, eye locations are updated for subsequent frames. The distinction of the proposed approach lies in that both eye detection and eye tracking are performed on the derived topographic manifold, rather than on an original-intensity image domain. The robustness of the approach is demonstrated under various imaging conditions and with different facial appearances, using both static images and video sequences without background constraints.