On-line signature recognition approach based on wavelets and Support Vector Machines

Abstract

In this work, a new on-line signature verification system is proposed. Firstly, the pen-position parameters of the online signature are decomposed into multiscale signals by using the wavelet transform technique. A TESPAR DZ based method is employed to code the approximation and details coefficients. Thus, for each analyzed time function, a fixed dimension feature vector is obtained. Experimental results were reported using the SVC2004 database. The models were trained and tested with the Support Vector Machine classifier. A feature level fusion strategy was adapted.