Face detection based on the manifold

Abstract

Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. It is a piece of cake to collect more than hundreds of thousands of examples from web and digital camera nowadays. How to train a face detector based on the collected immense face database? This paper presents a manifold-based method to select a training set. That is to say we learn the manifold from the collected enormous face data-base and then subsample and interweave the training set by the estimated geodesic distance in the low-dimensional manifold embedding. By the resulting training set, we train an AdaBoost-based face detector. The trained detector is tested on the MIT+CMU frontal face test set. The experimental results show that the proposed method based on the manifold is efficient to train a classifier confronted with the huge database. © Springer-Verlag Berlin Heidelberg 2005.