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

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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.

Accepted Answer

Greg Heath
Greg Heath on 25 Jan 2015
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
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

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