Signature recognition

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

Stability and repeatability of HMM based probability outputs across dynamic handwritten signature features

Abstract: This paper presents a study on the stability and repeatability of Hidden Markov Modeling based probability outputs across several different dynamic handwritten signature features. This study compares such values extracted from signature samples compiled within a single data collection session (i.e. between intra-session) and across several data collection sessions (i.e. between inter-sessions). The primary aim of this study is to investigate the indentifying capability of local online signature Hidden Markov Modeling based probability outputs which have implications on the accuracy of biometrics signature verification system which utilize similar HMM approach. This paper reports on an analysis results carried out on the online genuine signature counterparts of Sigma database - a compilation of over 6000 genuine signature samples that were gathered over a series of data collection sessions.

Biometric authentication device, biometric authentication method, and computer readable, non-transitory medium

Abstract: A biometric authentication device includes: a first biometric sensor that obtains biometric information of a user; a second biometric sensor that obtains biometric information of a user at a lower degree of reproducibility than the first biometric sensor; an authentication process unit that performs an authentication by comparing with use of the biometric information obtained by the first biometric sensor and the second biometric sensor, wherein the authentication process unit compares biometric information obtained by the second biometric sensor with use of biometric information obtained by the first biometric sensor of a case where a comparing between the biometric information obtained by the first biometric sensor and enrolled information is successful.

Hidden Markov models for speech and signal recognition.

Abstract: Hidden Markov methods have become the most widely accepted techniques for speech recognition and modeling. They are based on parametric statistical models which have two components. The first is a Markov chain which produces a sequence of states. This sequence of states characterizes the evolution of a non-stationary process like speech through a set of "short-time" stationary events. The sequence of states is "hidden" from the observer by the second component of the model, a set of output distributions, which governs the manner in which the sequence of states is converted into a sequence of speech observations. The purpose of this paper is first, to describe hidden Markov models (HMMs) as a general signal modeling procedure, second, to describe the application of HMMs to speech recognition and modeling, and, third, to describe other problems to which HMMs have been successfully applied. It is hoped that this discussion will inspire other applications, especially in the area of modeling biological processes represented as continuous waveforms, such as the electroencephalogram (EEG).

Flexible Neural Network Architecture for Handwritten Signatures Recognition

Abstract: This article illustrates modeling of flexible neural networks for handwritten signatures preprocessing. An input signature is interpolated to adjust inclination angle, than descriptor vector is composed. This information is preprocessed in proposed flexible neural network architecture, in which some neurons are becoming crucial for recognition and adapt to classification purposes. Experimental research results are compared in benchmark tests with classic approach to discuss efficiency of proposed solution.

Local Features Analysis of On-Line Signature Using Modified Distance of DTW

Abstract: Signature verification is a reliable and publicly acceptable method for authentication. Each signature is represented as a set of functional features such as coordinates of signature points, pen pressure and pen angle and therefore many features are available to the designer of signature verification system. The efficiency of any signature verification system depends mainly on the discrimination power and robustness of the features use in the system. This paper evaluates 40 dynamic features viewpoint classification error and consistency for extracting the best subset once a set of features provide maximal discrimination capability between genuine and forgery signatures. A modified distance of dynamic time warping (DTW) algorithm is proposed to improve performance of verification phase. The proposed system is evaluated on the public SVC2004 signature database. The experimental results show that first, the most discriminate and consistent features are velocity-based. Second, average equal error rate (EER) for proposed algorithm in comparison with the general DTW algorithm show a 47.5% decrease. Moreover, comparative study based on different classifier with skilled forgery show that the best result has EER of 1.73% using Parzen window classifier.