Analysis and improvement of face detection based on surf cascade

This paper aims to study limitations of the commonly employed boosting cascade framework. We focus on the factors like data, feature, weak classifier and stages. A set of novel experiments were done to show the relationship. The model contains three key points: SURF feature, weak classifier based on logistic regression and AUC-based cascade learning algorithm. This paper adds cross validation in logistic regression creatively which improves accuracy and speeds up convergence greatly. Eventually only five stages and about 100 weak classifiers are needed. The frontal face detector improves reject rate to 99% for the first three stages, decreases number of false positive greatly and achieves comparable performance among non-CNN techniques on FDDB dataset.