The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.

This work is a study about the fingerprint ridge distance and presents an efficient algorithm to estimate the values of local ridge frequency. Most of fingerprint image enhancement algorithms use an anisotropic filters with frequency response. Therefore, ridges frequency, as a global feature of fingerprint, is very important to image preprocessing methods used in automatic fingerprint identification system (AFIS). In proposed algorithm, through the use of fingerprint direction image, the frequency values are calculated directly, without the use of computationally expensive spectral analysis.