On-line signature recognition via fusion of dynamic features into dissimilarity space

In this paper a method for on-line signature recognition that combines dynamic features, fused into dissimilarity space, with a powerful collaborative sparse representation-based classification scheme is proposed. Dissimilarity vectors are formed in two stages. Initially, a number of informative dynamic features are extracted and stored in sequences. Afterwards, pairwise dissimilarities among feature sequences, computed using the DTW algorithm, are used to construct the new representation. Based on collaborative sparse representation principles, a new embedding space is defined where classification can be implemented efficiently. According to this scheme, signatures are represented in terms of their position inside the data structure, resulting in high-level performance without utilizing optimal feature selection procedures. The proposed framework has been evaluated using the SUSIG and the SVC2004 on-line signature databases.