CONF rodrig:2004:bioaw/IDIAP Boosting Pixel-based Classifiers for Face Verification Marcel, Sébastien Rodriguez, Yann EXTERNAL http://publications.idiap.ch/attachments/papers/2004/rodrig-bioaw-2004.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/rodrig2003 Related documents Biometric Authentication Workshop of the 8th European Conference on Computer Vision, BIOAW2004 2004 Springer-Verlag Prague, Czech Republic 24-31 The performance of face authentication systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for instance. Recently, boosting algorithms has emerged to boost the performance of simple (weak) classifiers by combining them iteratively. The famous AdaBoost algorithm have been proposed for object detection and applied successfully to face detection. In this paper, we investigate the use of AdaBoost for face authentication to boost weak classifiers based simply on pixel values. The proposed approach is tested on a benchmark database, namely XM2VTS. Results show that boosting only hundreds of classifiers achieved near state-of-the-art results. Furthermore, the proposed approach outperforms similar work on face authentication using boosting algorithms on the same database. REPORT rodrig2003/IDIAP Boosting Pixel-based Classifiers for Face Verification Rodriguez, Yann Marcel, Sébastien EXTERNAL http://publications.idiap.ch/attachments/reports/2003/rodrig-idiap-rr-03-65.pdf PUBLIC Idiap-RR-65-2003 2003 IDIAP Published in BIOAW Workshop of ECCV, 2004 The performance of face verification systems has steadily improved over the last few years. State-of-the-art methods use the projection of the gray-scale face image into a Linear Discriminant subspace as input of a classifier such as Support Vector Machines or Multi-layer Perceptrons. Unfortunately, these classifiers involve thousands of parameters that are difficult to store on a smart-card for instance. Recently, boosting algorithms has emerged to boost the performance of simple (weak) classifiers by combining them iteratively. The famous AdaBoost algorithm have been proposed for object detection and applied successfully to face detection. In this paper, we investigate the use of AdaBoost for face verification to boost weak classifiers based simply on pixel values. The proposed approach is tested on a benchmark database, namely XM2VTS. Results show that boosting only hundreds of classifiers achieved near state-of-the-art results. Furthermore, the proposed approach outperforms similar work on face verification using boosting algorithms on the same database.