Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

The Usability of Derived Function Features in Online Signature Verification

Abstract: Handwritten signatures are one of the most commonly used biometrics. Because signatures are widely accepted, their verification is a fundamental problem. The aim of signature verification is to decide about the origin of the signature so the ability to detect forgeries. Until offline signature verification is based on the scanned image of the signatures, online signature verification applies different electronic devices to capture the signatures. Online signatures also contain dynamic information such as the pressure or inclination angle of the pen, so it is much more challenging to forge online signatures than offline ones. In addition, it is possible to define and calculate further derived features based on the captured ones. The captured features are usually function features, which means they assign a value to each signature point or specified sets of signature points. This work aims to compare the usability of common derived function features using a dynamic time warping (DTW) based solution.

A new approach to signature recognition using the fuzzy method

Abstract: The Author(s) 2012. This article is published with open access at Springerlink.com Abstract The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding sig- nature's center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such struc- tures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.

Feature Recognition Method for Hand-Written Signature Image

Abstract: In recent years, the online payment by using credit card on Internet is still one of the major payment approaches. Although it is convenient and quick in use, there are lots of increasing demands in security solution. In this paper, we propose a feature recognition method to quickly verify whether user’s handwritten signature image is matched the record stored in the feature-based electronic handwritten-signature database (FEHS-DB) or not. The FEHS-DB is pre-established via records learning process. After the signing the signature by the user, the signature image is outputted as a same-size image. Then, the image will be taken its center point, and use the concentric circles in order to capture the intersection. Finally, these features are compared with the learned record in the FEHS-DB. The specific features retrieved by our scheme from the signature image could easily identify the signature is true or not. In addition, we show that the recognition speed is very fast than the traditional schemes.