Morphological Shared-Weight Neural Network for Face Recognition: logsigfx(t); - File Exchange

Code covered by the BSD License

Highlights from
Morphological Shared-Weight Neural Network for Face Recognition

... USAGE: splits the image function x into half:
[W,Z,O,E]=msnn1(t,xcell,noHid... This is a msnn backpropagation algorithm with 1 hiddenlayer
[WX,ZX,O,E]=msnn2(t,xcell,noH... This is a msnn backpropagation algorithm with 1 hiddenlayer
convolution(I,selectedeye) USAGE: this function performs convolution search on I
convolutionnex(I,selectedeye) USAGE: this function performs convolution search on I
draw_lines(L, X, Y, lt)
eyecrop_testim(im) USAGE: this function allows the user to select and crop
eyesearch(dataleft,dataright,...
eyesearch_amend(dataleft,data...
imline(I, p1x, p1y, p2x, p2y) This function draws lines on a grayscale image I
imrect(I,ltx,lty,rbx,rby) This function draws rectangles on a grayscale image I
logsigfx(t);
maxposition(I, n, range) This function retrieves the max n position
prescale(x)
tansigfx(t);
detect.m
detecteyes.m
geteyelevel.m
main.m
readimage.m
traindata_newfinal.m
traineyes_datafinal.m
traineyes_testSHIFT.m
traineyes_valfinal.m
traintest_newfinal.m
validation.m
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from Morphological Shared-Weight Neural Network for Face Recognition by Ethan Png
MSNN has the ability to learn feature extraction and perform classification at the same time.

logsigfx(t);

function [w] = logsigfx(t);

% USAGE: y = logsigfx(x)
% logsigfx returns the Boltzmann/logistic/Fermi function:
% y = 1 / ( 1 + exp(-x) )
% t is the face matrix of the system
% t can be a scalar or a matrix with
% the same dimension as x 


[ni,nj] = size(t);
un = ones(ni,nj);
w = un./(un+exp(-t));

Highlights from Morphological Shared-Weight Neural Network for Face Recognition

Highlights from
Morphological Shared-Weight Neural Network for Face Recognition