Improving 2D Boosted Classifiers Using Depth LDA Classifier for Robust Face Detection

摘要

Face detection plays an important role in Human Robot Interaction. Many of services provided by robots depend on face detection. This paper presents a novel face detection algorithm which uses depth data to improve the efficiency of a boosted classifier on 2D data for reduction of false positive alarms. The proposed method uses two levels of cascade classifiers. The classifiers of the first level deal with 2D data and classifiers of the second level use depth data captured by a stereo camera. The first level employs conventional cascade of boosted classifiers which eliminates many of nonface sub windows. The remaining sub windows are used as input to the second level. After calculating the corresponding depth model of the sub windows, a heuristic classifier along with a Linear Discriminant analysis (LDA) classifier is applied on the depth data to reject remaining non face sub windows. The experimental results of the proposed method using a Bumblebee-2 stereo vision system on a mobile platform for real time detection of human faces in natural cluttered environments reveal significantly reduction of false positive alarms of 2D face detector.