Face refinement through a gradient descent alignment approach
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Eye localization for face matching: is it always useful and under what conditions?
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
A Viewpoint Invariant, Sparsely Registered, Patch Based, Face Verifier
International Journal of Computer Vision
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Eye localization in low and standard definition content with application to face matching
Computer Vision and Image Understanding
Facial Feature Extraction and Change Analysis Using Photometric Stereo
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Optimal feature selection for support vector machines
Pattern Recognition
Regularized single-kernel conditional density estimation for face description
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Visual-context boosting for eye detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Subspace-based holistic registration for low-resolution facial images
EURASIP Journal on Advances in Signal Processing
Automatic localization of interest points in zebrafish images with tree-based methods
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Learning deformable shape manifolds
Pattern Recognition
Digital paparazzi: spotting celebrities in professional photo libraries
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
| Hi-index | 0.00 |
We address the task of accurately localizing the eyes in face images extracted by a face detector, an important problem to be solved because of the negative effect of poor localization on face recognition accuracy. We investigate three approaches to the task: a regression approach aiming to directly minimize errors in the predicted eye positions, a simple Bayesian model of eye and non-eye appearance, and a discriminative eye detector trained using AdaBoost. By using identical training and test data for each each method we are able to perform an unbiased comparison. We show that, perhaps surprisingly, the simple Bayesian approach performs best on databases including challenging images, and performance is comparable to more complex stateof- the-art methods.