Abstract. In this paper, a problem of personal authentication through the use of signature recognition is described. The methods of verification include both online (or dynamic) and off-line (static) signature verification algorithms. The dynamic methods covered, are based on the analysis of the shape, speed, stroke, pen pressure and timing information. While the static methods involve general shape recognition techniques. The paper gives a brief historical overview of the existing methods and presents some of the recent research in the field.

This paper accounts the development of a graduate course in biometrics at Purdue University. The course has been developed in conjunction with the inauguration of an applied biometrics research laboratory. The laboratory represents a Purdue-industry partnership through sponsored projects, advisory relationships, and financial support, industry partners assure that the laboratory achieves its potential in preparing students to perform effectively in the quickly evolving biometrics environment. The laboratory also serves as an educational resource for industry.

Dynamic signature recognition is one of the commonly used biometric traits. In this paper we propose use of Gabor filters based feature for verification of dynamic signature. We incorporate the timing information available in the signature along with the Gabor filter response to generate the feature vector. Gabor filters have been widely used for image, texture analysis. Here we present analysis for the Gabor filter based feature vector of a dynamic signature.

Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system. Categories and Subject Descriptors I.4.7 Image Processing and Computer vision.