Description: 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.
File list (Check if you may need any files):
人脸检测（文章+程序）\2.BMP
.....................\2.jpg
.....................\3899722520070904110235851_007_640.jpg
.....................\FACE DETECTION USING LOCAL SMQT FEATURES AND SPLIT UP SNOWCLASSIFIER.pdf
.....................\facefind.dll
.....................\face_detect.asv
.....................\face_detect.m
.....................\Thumbs.db
人脸检测（文章+程序）
Download