Artificial Neural Network (ANN) is a branch of artificial intelligence theory that has been
used in various applications such as pattern recognition. The advantages of ANN as a system
is the ability to imitate human thoughts in computational intelligence such as pattern
recognition. ANN is useful to do modelling prediction, error detection and control systems
with artificial intelligence approaches and computational design.
There are 3 methods that commonly used in ANN heuristic rule, delta-delta rule, and delta-
bar-delta rule. Delta-bar-delta rule that use by backpropagation method is the best algorithm
to solve the problem input to the network [5]. By applying learning rate [3] in
backpropagation algorithm, learning process will be more stable and faster in finding the
optimal in the delta (stepsize) by reducing error for optimal solution. Shao and Zheng [4]
apply momentum in backpropagation algorithm and the result shows that the error sequence
is monotonously decreased during the training procedure and the algorithm is weakly
convergent, the gradient of error sequence converges to zero as the training iteration goes on.
Fingerprint is one of Biometric identity measurement using pattern recognition that is
important to determine the accuracy of personal identification. Fingerprints had strong nature
of unchangeable over time and each person is different from the others from one person to
another. Conventional biometric fingerprint technology sometimes is inaccurate because the
fingerprint position is alterated in scanner tools. This disadvantage can be minimize using
ANN method with Backpropagation algorithm. Fingerprint recognition using standard
backpropagation shows 66,91% average accuracy and 225 seconds of average training time.
The accuracy increases by adding momentum and learning rate with gradual value in
Backpropagation algorithm. Average accuracy of 80,9% can be achieved using combination
of momentum and learningrate, and 144 seconds average training time.
Keywords: Neural Networks, fingerprint patterns, Backpropagation, momentum, learningrate