双阈值级联分类器的加速人脸检测算法

提出了一种基于双阈值的两级级联分类器的人脸检测加速方法。该方法首先应用Gabor滤波器提取经模板匹配保留的似人脸样本特征，经主成分分析（PCA）降维后的特征作为第一级BP神经网络输入进行检测，在输出端应用双阈值对人脸/非人脸进行粗检测，然后把介于双阈值之间的人脸/非人脸模块作为第二级AdaBoost算法设计的输入并再次进行精检测，从而在提高检测速度的同时达到提高检测率和降低误检率的目的。实验表明，应用双阈值进行级联分类加速检测后，该方法的检测精度要优于基于简单阈值的分类器。

Accelerated algorithm of face detection based on dual-threshold cascade classifiers

The paper proposed an accelerating way of face detection based on dual-threshold cascade classifiers. First, it applied Gabor filter to extract the face-like features that were retained by template matching, then put eigenvectors extracted by the way of Principal Component Analysis (PCA) into the BP neural network as first classifier, then used dual-threshold to decide face or non-face on output end, and put the face or non-face of midway between up and down threshold into the AdaBoost classifier as the second classifier to decide. In this way, it can improve the detection rate and reduce the false rate while speeding up the detection speed. The experimental results prove that the precision of cascade classifier of face detection based on dual-threshold is superior to the classifier of single threshold.