Multi-view face detector using a single cascade classifier

Abstract

In this work, a cascade classifier is trained to detect multi-view face samples. Comparing with most of face detection system which use different classifier to classify frontal face and profile face, our system has advantage in detection speed. The proposed face detector extracts the Haar-like feature from the training samples and train a cascade classifier by using Adaboost learing algorithm. Different from the existing algorithms, our detection system only contains a cascade classifier model. Our preliminary experiments demonstrate that our cascade classifier can achieve similiar accuracy and 60% higher speed detection than the multi-view face detection system which consist of two sparate cascade classifiers.