Grid-based feature distributions for off-line signature verification

• Published in: IET biometrics Vol. 1; no. 1; pp. 72 - 81
• Main Authors: TSELIOS, K, ZOIS, E. N, SIORES, E, NASSIOPOULOS, A, ECONOMOU, G
• Format: Journal Article
• Language: English
• Published: Stevenage Institution of Engineering and Technology 01.03.2012; John Wiley & Sons, Inc
• Subjects: Algorithms; Applied sciences; Classification; Cryptography; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Feature extraction; Information, signal and communications theory; Pattern recognition; Pixels; Representations; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Signatures; Similarity; Telecommunications and information theory; Zoning; Performance evaluation; Discriminant analysis; Automatic classification; Dimensionality; Similarity; Error rate; Support vector machine; Pattern recognition; Algorithm; Signal classification; Implementation; Handwriting recognition; Authentication; Signal processing; Feature extraction; Off line; Handwritten character recognition; Manuscript character
• ISSN: 2047-4938; 2047-4946
• DOI: 10.1049/iet-bmt.2011.0011

In this work, a feature extraction method is presented for handwritten signature verification. The proposed algorithm models the handwritten elements of a signature trace by probabilistically counting the distribution of fixed two- and three-step pixel paths, conditioned that they are confined within predetermined Chebyshev distances of two and three, respectively. This representation correlates the pixel transitions along the signature trace, with the writing style of an individual. Various partitions of the signature image into a group of sub-images were applied in order to define the overall dimensionality of the feature. In order to evaluate the classification efficiency of the introduced method, a number of verification strategies are implemented by making use of two internationally accepted and one domestic datasets. In all schemes, similarity scores and hard margin support vector machines (SVMs) are combined or evaluated as separate entities. Additionally, zoning the extracted feature vector into combinations of tetrads and heptads, which in turn are fed into the afore-mentioned classification schemes, is exploited. Results, derived from random or simple imitations as well as simulated (skilled) forgery indicate that the proposed method achieves noticeably low equal error rates and it is expected to provide a powerful discriminative representation of the handwritten signature.