Score level fusion approach in dynamic signature verification based on hybrid wavelet-Fourier transform

ABSTRACTIn this work, a dynamic handwritten signature verification system based on hybrid discrete wavelet transform and discrete Fourier transform is presented. Wavelet transform is adopted to analyze the signature information in a multi‐resolution representation while retaining its local information by decomposing the signature into different sub‐bands. Fourier transform is performed to extract the feature descriptor of the decomposed sub‐bands. A dissimilarity score of the extracted features between the test signature and reference data is computed using Euclidean distance and enveloped Euclidean distance. Apart from that, several score level fusion mechanisms have been investigated to combine decisive information to boost up the system performance. The k‐nearest neighbor and support vector machine are applied in order to fuse multiple features. The resulting score value is then normalized and compared with a threshold value in order to decide whether a given signature is genuine or forgery. Experiments are conducted on a released version of benchmark SVC2004 database. Two datasets (Task 1 and Task 2), which have different types of signature information, are used to evaluate the proposed system, and promising verification results are achieved. Copyright © 2013 John Wiley & Sons, Ltd. In this work, a dynamic handwritten signature verification system based on hybrid discrete wavelet transform and discrete Fourier transform is presented. The k‐nearest neighbor and support vector machine are applied to fuse multiple features at score level for evaluation purpose. Experiments are conducted on a released version of benchmark SVC2004 database, and the verification results show the proposed method is feasible in practice.