"A new signature verification technique based on a two-stage neural network classifier"
Engineering Applications of Artificial Intelligence, Vol. 14, pp. 95-103, 2001
This paper presents a new technique for off-line signature recognition and verification. The proposed system is based on global, grid and texture features. For each one of these feature sets a special two stage Perceptron OCON (one-class-one-network) classification structure has been implemented. In the first stage, the classifier combines the decision results of the neural networks and the Euclidean distance obtained using the three feature sets. The results of the first-stage classifier feed a second-stage radial base function (RBF) neural network structure, which makes the final decision. The entire system was extensively tested and yielded high recognition and verification rates.