Signature recognition

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

Analysis on compact data formats for the performance of handwritten signature biometrics

Abstract: This paper deals with the Signature Data Formats proposed by ISO 19794 project: 19794 part 7 Full Format and Compact Format (published in 2007) and the new 19794 part 11, which is under development. It will be shown how these formats handle the raw data coming from a Signature Input Device, and what the size of a Biometric Information Record is for each one. Another compression method, using LZ77 compression algorithm, is proposed and tested. The paper will also show the impact of using these compact formats on the performance of two different algorithms: Dynamic Time Warping and Gaussian Mixture Models. MCyT and SVC2004 signature databases have been used to carry out all tests.

CSFNN optimization of signature recognition problem for a special VLSI NN chip

Abstract: In this paper, a Conic Section Function Neural Network (CSFNN) based system for signature recognition problem is developed. The purpose of this work is to optimize CSFNN parameters for signature recognition problem to be applied to the VLSI Neural Network (NN) chip. Signature database is constructed after some preprocessing techniques are applied on collected raw data. After the preprocessing phase, the database is introduced to the CSFNN. Then CSFNN parameters are optimized to obtain acceptable signature recognition accuracy for a compact NN chip. Simplicity of the CSFNN structure and the range of parameters make CSFNN suitable for hardware implementation for this problem.

On-Line Handwritten Signature Verification Algorithm Based on Time Sequence

Abstract: On-line handwritten signature verification is a topic in the field of biometric authentication now. Time sequence match and result judgement are the key to this problem. This paper presents a time sequence match algorithm for handwritten signature verification. The algorithm is effectual to be examined by signature data from SVC 2004(First International Signature Verification Competition). The paper also introduces distance calculation formula based on weighting H1 module to calculate the differences between input signatures and genuine signatures.

Off-Line Signature Verification Using Two Step Transitional Features

Abstract: In this work, a new approach for off-line signature recognition and verification is presented and described. A subset of the line, concave and convex family of curvature features is used to represent the signatures. Two major constraints are applied to the feature extraction algorithm in order to model the two step transitional probabilities of the signature pixels. Segmentation of the signature trace is enabled using a window which is centred upon the centre of mass of the thinned image. Partitioning of the image leads to a multidimensional feature vector which provides useful spatial details of the acquired handwritten image. The classification protocol followed in this work relies on a hard margin support vector machine. Our method was applied to two databases, the first taken from the literature while the second created by the authors. In order to provide comparable results for the first stage signature verification system, we have applied an already published feature extraction method while keeping the same classification protocol. Primary evaluation schemes on both corpuses provide very encouraging verification results for the Average Error.

3D Signature for Efficient Authentication in Multimodal Biometric Security Systems

Abstract: Unimodal biometric systems rely on a single source of biometric trait information for recognition of individuals. These systems are highly vulnerable to spoof attacks as imposters easily imitate the particular biometric trait of any genuine user. The impact of circumvention is reduced by combining the functions of different unimodal biometric systems to perform as a multi-biometric system. The multimodal biometric systems operate in two or more ways to authenticate individuals by their biometric traits. This paper proposes a multimodal biometric security model for efficient authentication. The model deals with multi-biometrics in first two phases for identification, verification followed by the decision making as third phase. The first phase employs physiological biometric traits for identification by exhibiting the liveliness of individual. The second phase uses 3D handwritten signature for verification of the claiming identity. The 3D handwritten signature records the pressure information on the special signature pad during the signing process. The pressure information recorded on different layers of the signature pad provides distinct information for verification of the individuals based on their signatures. This unique pressure information raises the level of difficulty in the forgery of signatures. The individual matching score is calculated in identification phase and verification phase. The fusion is performed on the obtained matching scores and compared with threshold value in the decision phase to provide efficient authentication of the individual. The threshold value in the decision phase is varied according to particular application for combating the problem of circumvention in biometric security systems. The preliminary results show the viability of using 3D handwritten signature in biometric security.