A Novel Online Signature Verification System Based on GMM Features in a DTW Framework

This paper presents a novel online signature verification system based on the extension of the traditional dynamic time warping (DTW) matching scheme. We propose the use of a set of features derived from a Gaussian mixture model (GMM) for the alignment of the signatures using DTW. These features aid in capturing signature-dependent characteristics of a user in the feature space with a probabilistic framework. In addition, we explore the characteristics of the warping path of DTW, by employing the proposed GMM features. We derive a score for the warping path, and fuse it to that of the DTW score for verifying the authenticity of a test signature. To the best of our knowledge, this paper is the first of its kind that uses the features of the GMM, a model-based classifier into the framework of the DTW technique for online signature verification. The experiments are conducted on the publicly available MCYT database for both common and user thresholds. The results obtained are promising over prior works for this database.