Abstract：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.
王燕 公维军. 双阈值级联分类器的加速人脸检测算法[J]. 计算机应用, 2011, 31(07): 1822-1824.
Yan WANG Wei-jun GONG. Accelerated algorithm of face detection based on dual-threshold cascade classifiers. Journal of Computer Applications, 2011, 31(07): 1822-1824.
ROWLEY H A, BALUJA S, KANADA T. Neural network-based face detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(1):23-38.
[2]
VIOLA P, JONES M. Rapid object detection using a boosted cascade of simple features[C]// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2001:511-518.
[3]
LIENHART R, MAYDT J. An extended set of haar-like features for rapid object detection[C]// Proceedings of the International Conference in Image Processing. New York:IEEE, 2002:900-903.
[4]
HEISELE B, SERRE T, POGGIO T. A component-based framework for face detection and identification[J]. International Journal of Computer Vision, 2007, 74(2):167-181.〖HJ1.45mm〗
[5]
HSU R L, ABDEL-MOTTALEB M, JAIN A K. Face detection in color images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5):696-706.
2011, Vol. 31 
