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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07 Nov 2002
TL;DR: A new classification scheme is built for one particular biometric scheme, handwriting, to give individual users with a specific application in mind orientation and a decision tool.
Abstract: A wide variety of biometric based techniques have been proposed but it is quite difficult to classify the approaches according to their application domains and to measure their functionality. Our intention is to classify today's applications in detail for one particular biometric scheme, handwriting. To give individual users with a specific application in mind orientation and a decision tool, we have built a new classification scheme and furthermore define major characteristics for each of the application classes as an evaluation matrix.
12 citations
05 Oct 1999
TL;DR: This paper has proposed a multi-template matching approach to identify the individual via few training samples to achieve a better performance if more training samples are collected.
Abstract: Signature verification is a natural and friendly approach in biometrics-based verification. As we know, the system can achieve a better performance if more training samples are collected. However, routinely signing the patterns is a boring and inconvenient process to obtain enough training samples at the initial enrolment. In this paper, we have proposed a multi-template matching approach to identify the individual via few training samples. Some experimental results were conducted to show the effectiveness of our proposed methods.
11 citations
01 Sep 2000
TL;DR: A robust multifont character recognition system for degraded documents, such as photocopy or fax, is described and clearly outperforms commercial systems and leads to further error rate reductions compared to previous results reached on this database.
Abstract: A robust multifont character recognition system for degraded documents, such as photocopy or fax, is described. The system is based on hidden Markov models using discrete and hybrid modeling techniques, where the latter makes use of an information theory-based neural network. The presented recognition results refer to the SEDAL-database of English documents using no dictionary. It is also demonstrated that the usage of a language model that consists of character n-grams yields significantly better recognition results. Our resulting system clearly outperforms commercial systems and leads to further error rate reductions compared to previous results reached on this database.
11 citations
01 Jan 2001
TL;DR: Only some more simple (statistical) forms of biometric and biomedical information have found their application when person identification, and raised interest for these methods of identification can be caused by new possibilities of information technologies.
Abstract: . Many biometric methods are closely connected with methods of patternrecognition and image analysis. The realization of a number of biometric technologiesrequires using the last achievements in this area. Some elements of technology based onsome methods of image analysis are demonstrated by the example of iris personidentification. From a position of organizing the educational process, laboratory works in thearea of biometric technologies allow stimulating students’ inquisitiveness in studying methodsand algorithms for image processing and pattern recognition. Key words: biometric technologies, biometric authentication, image processing,education 1. Introduction Biometric and biomedical informatics are the fast developing scientific direction, studying theprocesses of creation, transmission, reception, storage, processing, displaying and interpretationof information in all the channels of functional and signal systems of living objects which areknown to biological and medical science and practice. Modern natural sciences at presentsharply need in the updating of scientific picture of the world, and the essential contribution inthis process can be made by the biometric and biomedical methods.Only some more simple (statistical) forms of biometric and biomedical information have foundtheir application when person identification, and raised interest for these methods ofidentification can be caused by new possibilities of information technologies.So, exclusively new and not explored possibilities for verification of living objects can beexpected in eniology. A concept of electromagnetic is intensively investigated at present. Newresults have been obtained in fractal analysis, using which an attempt was taken to explainsome paradoxical phenomena such as morphogenetic field, distant cells communications,anomalously high sensitivity of organism to near-zero frequency perturbations, regulationprocesses critical dependence on the fractal features of noisy environment, etc. [Polo]. Perhaps,
11 citations
12 Aug 2011
TL;DR: A new technique for source signal separation that relies on a single sEMG sensor is presented that was employed in a classification framework for hand movements that achieved comparable results to other approaches in the literature, but yet, it relied on a much simpler classifier and used a very small number of features.
Abstract: Rehabilitation devices, prosthesis and human machine interfaces are among many applications for which surface electromyographic signals (sEMG) can be employed. Systems reliant on these muscle-generated electrical signals require various forms of machine learning algorithms for specific signature recognition. Those systems vary in terms of the signal detection methods, the feature selection and the classification algorithm used. However, in all those cases, the use of multiple sensors is a constant. In this paper, we present a new technique for source signal separation that relies on a single sEMG sensor. This proposed technique was employed in a classification framework for hand movements that achieved comparable results to other approaches in the literature, but yet, it relied on a much simpler classifier and used a very small number of features.
11 citations