本文应用SMQT和 SPLIT UP SNOW 分类器来完成对人脸的检测。-The purpose of this paper is threefold: firstly, the local Successive

Mean Quantization Transform features are proposed for illumination

and sensor insensitive operation in object recognition. Secondly, a

split up Sparse Network of Winnows is presented to speed up the

original classifier. Finally, the features and classifier are combined

for the task of frontal face detection. Detection results are presented

for the MIT+CMU and the BioID databases. With regard to this

face detector, the Receiver Operation Characteristics curve for the

BioID database yields the best published result. The result for the

CMU+MIT database is comparable to state-of-the-art face detectors.