MATLAB: How to decide inputs and targets for neural networks for a signature recognition and verification system

Deep Learning Toolboxhttp://www.mathworks.com/matlabcentral/answers/45252-how-to-decide-inputs-and-targets-for-neural-networks-for-a-signature-verification-system

Hello!
I am doing project on offline signataure verification using neural network. I have prepared the database of 360 signatures(8 genuine and 4 forge signatures of each of the 30 person) and extracted features (moments of image using Zernike moments) of each signature. But I dont know how to train the neural network so that it can recognize the genuine and forge signatures. thanks.

Best Answer

For N=360 examples of I-dimensional extracted feature column vectors and corresponding N-dimensional row vector of class indices i (1<=i<=c=30), the target matrix is ind2vec(indices) and
 [ I N ] = size(input)  % [ I  360 ]
 [ c N ] = size(target) % [ 30 360 ]
The default number of training vectors is
 Ntrn = N -  2*round(0.15*N) % 252
yielding
 Ntrneq = Ntrn*c     % 7560
training equations. When the number of hidden nodes, H satisfies
 H << Hub = -1+ceil( (Ntrneq-c)/(I+c+1))
The the number of weights
 Nw = (I+1)*H+(H+1)*c
is much less than the number training equations. Otherwise validation stopping and/or regularization are recommended.
Hope this helps.
Thank you for formally accepting my answer
Greg
Related Question