Signature Recognition Using Clustering Techniques: Vector Quantization based Approach for Static Signature Recognition

Biometric authentication techniques are in high demand for entrance monitoring and security systems. The techniques must be cheap, reliable and, simple. Handwritten signature verification satisfies these requirements. Signature Recognition is a very well known area in Biometrics. Signature of a person is one of the important biometric attribute, has been used for centuries as an authentication measure. In current era signatures are important in business, banking, legal application areas. With the tremendous developments in computer technology and advancements in programming platforms the field of biometrics has seen increments with leaps and bounds. In this book we have discussed an automatic off-line signature verification and forgery detection system based on clustering technique. This system uses the Vector Quantization, Walsh Coefficients, Geometric centers, Grid and Texture features as well as local and Global features of a static handwritten signature.