Face recognition using back propagation network.

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I am trying to implement face recognition system. I am extracting the zernike features. the length of my feature vector is 49. on using euclidean distance as the classifier, I am getting an accuracy of 94%. however, on using BPN, I am getting just 89%. I am not sure if I am doing it right. I used "patternnet" in MATLAB as:
nat=patternnet(48);
nat.trainFcn='trainscg';
%nat.trainParam.lr=0.01;
nat.trainParam.epochs = 4000;
nat.performFcn = 'sse';
nat.trainParam.min_grad = 1e-11;
%nat.trainParam.goal=1e-11;
nat.divideFcn = 'dividerand';
nat.divideParam.trainRatio=100/100; nat.divideParam.valRatio=0; nat.divideParam.testRatio=0;
[nat,tr]=train(nat,A,t);
Is there any other parameter that I should set?
I also tried to implement the BPN through code. My code is working fine for XOR net. But I am not understanding how to use it for Zernike features. Please help.

Accepted Answer

Greg Heath
Greg Heath on 19 Dec 2013
How many examples do you have?
How many classes?
The number of hidden nodes should probably be much smaller than 48
Why aren't you using as many defaults as possible?
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greg patternnet
Hope this helps.
Thank you for formally accepting my answer
Greg
  9 Comments
Geetika
Geetika on 21 Dec 2013
I was capable of implementing my own BPN network as well as built in function and still getting low accuracy. For RBF and PNN, I am using the inbuilt tools.
Greg Heath
Greg Heath on 22 Dec 2013
Did you normalize? Reduce the dimensionality? Vary the spread in RBF and PNN? Vary the number of hidden nodes in the MLPNN (There is no such animal as a BPN) How much data for training? How many random weight initialization trials? Are you still using 'dividetrain'? If not, what split ratio?

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