Off-line signature verification using an auto-associator cascade-correlation architecture

In this paper, an auto-associator neural network based on the constructive cascade correlation architecture (Cascor) is investigated on an real-world signature verification problem. The traditional multilayer-perceptron trained with backpropagation is also examined in the same problem and a experimental comparison is conducted to evaluate the two network's generalization performances. The main objective is to show that constructive networks, in this case represented by the cascade-correlation, can offer in some situations a real alternative to the traditional models for the solution of practical problems. The experimental results indicate that the constructive network investigated can be efficiently applied to difficult real world pattern verification problems.