2.2.2. SN

SN [31], available in MATLAB (http://www.mathworks.com/matlabcentral/fileexchange/loadFile. do?objectId=13701&objectType=FILE), feeds SMQT features, as briefly discussed in the Introduction, to a Split up Sparse Network of Winnows (SN) classifier. SMQT enhances gray-level images. This enhancement reveals the structure of the data and additionally removes some negative properties such as gain and bias. This is how SMQT features overcome to some extent the illumination and noise problem.

SMQT features are extracted by moving a patch across the image while repeatedly downscaling and resizing it to detect faces of different sizes. The detection task is performed by the SN classifier, i.e., a sparse network of linear units over a feature space that can be used to create lookup tables.
