On-Line Signature Recognition Based on Reduced Set of Points

In this paper, a novel online signature verification technique based on discrete cosine transform (DCT) and sparse representation is proposed. We find a new property of DCT, which can be used to obtain a compact representation of an online signature using a fixed number of coefficients, leading to simple matching procedures and providing an effective alternative to deal with time series of different lengths. The property is also used to extract energy features. Furthermore, a new attempt to apply sparse representation to online signature verification is made, and a novel task-specific method for building overcomplete dictionaries is proposed, then sparsity features are extracted. Finally, energy features and sparsity features are concatenated to form a feature vector. Experiments are conducted on the Sabancı University's Signature Database (SUSIG)-Visual and SVC2004 databases, and the results show that our proposed method authenticates persons very reliably with a verification performance which is better than those of state-of-the-art methods on the same databases.

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On-Line Signature Recognition Based on Reduced Set of Points

Cited by 3 publications

References 10 publications

A Hybrid System of Signature Recognition Using Video and Similarity Measures

A Hybrid System of Signature Recognition Using Video and Similarity Measures

Online signature verification based on signatures turning angle representation using longest common subsequence matching

Online Signature Verification Based on DCT and Sparse Representation

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