S Signature Recognition Using Conjugate Gradient Neural Networks

signatures: the functional approach and the parametric approach. This paper presents a new approach for dynamic handwritten signature verification (HSV) using the Neural Network with verification by the Conjugate Gradient Neural Network (NN). It is yet another avenue in the approach to HSV that is found to produce excellent results when compared with other methods of dynamic. Experimental results show the system is insensitive to the order of base-classifiers and gets a high verification ratio. I. INTRODUCTION IGNATURE verification is to evaluate whether a suspected signature is genuine or forgery. It's widely used in the fields of finance and security. Usually three kinds of forgery can happen in signature verification. Random forgery is taking the genuine signature of others for that of the current user. Skilled forgery is produced with close imitations. It is hard to be discriminated from the genuine one only by shape variations. Simple forgery is produced with the knowledge of content but without close imitations. For example, the forger signs out of his/her memory on the genuine signature. Many systems for HSV have been proposed in the literature. Sabourin and Drouhard [1] presented a method based on directional probability density functions together with BP neural networks to detect random forgery. Qi and Hunt [2] used global and grid features with a simple Euclidean distance classifier. In this paper, multiple classifiers integration using the Neural Network with verification by the Conjugate Gradient Neural Network (NN) algorithm is proposed. This system is designed to detect both random and simple forgeries. In the rest part of Bajaj and Chaudhury[3] proposed a system consisting of sub-classifiers that are based on three sets of global features. Sansone and Vento [4] proposed a sequential three-stage multi-expert system, in which the first expert eliminates random and simple forgeries, the second isolates skilled forgeries, and the third gives the final decision by combining decisions of the previous stages together with