Regression and classification approaches to eye localization in face images

Conference item

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 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 state-of-the-art methods

Authors: Everingham, M; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: 7th International Conference on Automatic Face and Gesture Recognition (FGR06)
• Pages: 441-446
• Publication date: 2006-04-24
• Event title: 7th International Conference on Automatic Face and Gesture Recognition (FGR06)
• Event location: Southampton, UK
• Event start date: 2006-04-10
• Event end date: 2006-04-12
• DOI: 10.1109/fgr.2006.90
• ISBN: 0769525032
• Language: English
• Keywords: support vector machines; image recognition; face recognition; eyes; face detection; testing; predictive models; bayesian methods; image databases; detectors