SMQT特征检测和分割人脸检测识别

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.

人脸检测（文章+程序）
facefind.dll
FACE DETECTION USING LOCAL SMQT FEATURES AND SPLIT UP SNOWCLASSIFIER.pdf
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face_detect.m