Topic
Signature recognition
About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.
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29 Mar 2005TL;DR: In this article, an observation vector as input data, which represents a certain point in the observation vector space, is mapped to a distribution having a spread in the feature vector space and a feature distribution parameter representing the distribution is determined.
Abstract: It is intended to increase the recognition rate in speech recognition and image recognition. An observation vector as input data, which represents a certain point in the observation vector space, is mapped to a distribution having a spread in the feature vector space, and a feature distribution parameter representing the distribution is determined. Pattern recognition of the input data is performed based on the feature distribution parameter.
39 citations
TL;DR: Issues regarding off-line signature recognitions are discussed, a system designed using cluster based global features which is a multi algorithmic offline signature recognition system is discussed and existing techniques are reviewed.
Abstract: Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document. In this paper we discuss issues regarding off-line signature recognitions. We review existing techniques, their performance and method for feature extraction. We discuss a system designed using cluster based global features which is a multi algorithmic offline signature recognition system.
39 citations
TL;DR: Experimental results show that the proposed protected on-line signature recognition system guarantees recognition rates comparable with those of unprotected approaches, and outperforms already proposed protection schemes for signature biometrics.
Abstract: In this paper we propose a biometric cryptosystem able to provide security and renewability to a function based on- line signature representation. A novel reliable signature traits selection procedure, along with a signature binarization algorithm, are introduced. Experimental results, evaluated on the public MCYT signature database, show that the proposed protected on-line signature recognition system guarantees recognition rates comparable with those of unprotected approaches, and outperforms already proposed protection schemes for signature biometrics.
39 citations
07 Nov 2009
TL;DR: The distance between two signatures is computed by dynamic time warping (DTW) method and the reference signatures are used to assign special parameters for each signer, which makes the system cover the intra signer variation.
Abstract: This work describes an enhanced technique for on-line signature verification. The distance between two signatures is computed by dynamic time warping (DTW) method. The reference signatures are used to assign special parameters for each signer, which makes the system cover the intra signer variation. Several features are extracted. Systems with single and multi-features are tested. Curvature change and speed enhance success verification rate. The experiments have been carried out using the SUSIG online signature database. The best result for ROC area under curve is 99.5 with equal error rate 3.48%, and the best result for equal error rate is 3.06% with ROC area under curve 99.43.
39 citations
01 Jan 2004
TL;DR: This is the first paper where this novel approach -called tied posteriors- for handwriting recognition is presented, and the usage of a language model, that consists of character n-grams, as an alternative to the recognition with a large dictionary of German words is demonstrated.
Abstract: In this paper a system for on-line cursive handwriting recognition is described. The system is based on Hidden Markov Models (HMMs) using discrete and hybrid modeling techniques. Here, we focus on two aspects of the recognition system. First, we present different hybrid modeling techniques, whereas one depends on an information theory-based neural network (MMI-criterion) used as a vector quantizer and the other uses a neural net for estimating the a posteriori probabilities to replace the codebook of a tied-mixture HMM system. This is the first paper where we present this novel approach -called tied posteriors- for handwriting recognition. Second, we demonstrate the usage of a language model, that consists of character n-grams, as an alternative to the recognition with a large dictionary of German words. Our resulting system for character recognition yields significantly better recognition results using an unlimited vocabulary.
39 citations