此文的目的有三个：第一，当地连续均值量化变换特征是提出照明和传感器敏感操作在目标识别上。其次，注册稀疏Winnows网络分割，提出了加快原分类。最后，特点和分类相结合对于正面人脸检测任务。检测结果列

为MIT + CMU系统和BioID数据库。关于这人脸检测器，接收器操作特征曲线BioID数据库产生最好的结果公布。对于结果麻省理工学院的中央结算系统+数据库相当于国家的最先进的脸探测器。一个人脸检测算法的MATLAB版本可以从http://www.mathworks.com/matlabcentral/fileexchange/

loadFile.do？的ObjectID = 13701＆的objectType =FILE下载。

-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.

A Matlab version of the face detection algorithm can be downloaded

from http://www.mathworks.com/matlabcentral/fileexchange/

loadFile.do?objectId=13701&objectType=FILE.