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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01 Nov 2014
TL;DR: This study has represented words by factorizing two streams in different manners, where the interaction is achieved through the causal influence between observable variables in the first model and state variable in the second one, and compared the two models on the recognition of off-line Arabic handwritten words.
Abstract: Hidden Markov Models (HMMs) are now widely used for off-line Arabic handwriting recognition. Actually, classical HMMs are one-dimensional models, that is why to process an Arabic word image we have developed a discrete Dynamic Bayesian Network (DBN). The DBNs are an extension and a generalization of the classical HMMs, which can model the interaction between several observations and state sequences. In our study, we have represented words by factorizing two streams in different manners, where the interaction is achieved through the causal influence between observable variables in the first model and state variables in the second one. The aim of this is to consider the two flows of information together: The observations on the columns (as well as lines) are obtained by scanning the image horizontally (and also vertically) by a uniform sliding window. We have compared the two models on the recognition of off-line Arabic handwritten words. The experiments show that the first model is better and more adapted to our task than the second one.
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30 Aug 1992
TL;DR: The main aim of the system is to perform an object recognition based on contour description and being independent of noise and indeterminations in the digitization process.
Abstract: The interest in fuzzy algorithms is increasing in a wide range of pattern recognition applications. This paper describes a recognition system using fuzzy algorithms and parameters, with particular enhancement in the system architecture. This system is able to recognize flat polygonal objects in real time. The main aim of the system is to perform an object recognition based on contour description and being independent of noise and indeterminations in the digitization process. This system is made up of three modules. The first one is a break points detector. The second block performs a contour description by means of fuzzy parameters that allows us to recognize the picture in the third block. >
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TL;DR: A robust watermarking scheme using the sparse information of watermark biometric to secure vulnerable point like protection of biometric templates at the communication channel ofBiometric authentication systems is proposed.
Abstract: Biometric based human authentication system is used for security purpose in many organizations in the present world This biometric authentication system has several vulnerable points Two of vulnerable points are protection of biometric templates at system database and protection of biometric templates at communication channel between two modules of biometric authentication systems In this paper proposed a robust watermarking scheme using the sparse information of watermark biometric to secure vulnerable point like protection of biometric templates at the communication channel of biometric authentication systems A compressive sensing theory procedure is used for generation of sparse information on watermark biometric data using detail wavelet coefficients Then sparse information of watermark biometric data is embedded into DCT coefficients of host biometric data This proposed scheme is robust to common signal processing and geometric attacks like JPEG compression, adding noise, filtering, and cropping, histogram equalization This proposed scheme has more advantages and high quality measures compared to existing schemes in the literature
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TL;DR: Tests on CASIAv3 image database have resulted in a 2% accuracy improvement with respect to traditional methods; a significant one in iris recognition.
Abstract: Iris recognition is regarded as the most reliable and accurate biometric identification system available. The present work involves the development of a novel technique in order to improve the performance of iris recognition systems. We have used for our experiments a publicly available iris recognition system. Tests on CASIAv3 image database have resulted in a 2% accuracy improvement with respect to traditional methods; a significant one in iris recognition.
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TL;DR: The problem of secured biometric authentication as the problem of finding capacity and rate-distortion curve for a secured biometrics authentication system is restated and transductive methods from statistical learning theory are used to estimate the conditional error probabilities of the system.
Abstract: This paper provides an information theoretical description of biometric systems at the system level. A number of
basic models to characterize performance of biometric systems are presented. All models compare performance of
an automatic biometric recognition system against performance of an ideal biometric system that knows correct
decisions. The correct decision can be visualized as an input to a new decision system, and the decision by an
automatic recognition system is the output of this decision system. The problem of performance evaluation for
a biometric recognition system is formulated as (1) the problem of finding the maximum information that the
output of the system has about the input, and (2) the problem of finding the maximum distortion that the output
can experience with respect to the input of the system to guarantee a bounded average probability of recognition
error. The first formulation brings us to evaluation of capacity of a binary asymmetric and M-ary channels. The
second formulation falls under the scope of rate-distortion theory. We further describe the problem of physical
signature authentication used to authenticate a biometric acquisition device and state the problem of secured
biometric authentication as the problem of joint biometric and physical signature authentication. One novelty
of this work is in restating the problem of secured biometric authentication as the problem of finding capacity
and rate-distortion curve for a secured biometric authentication system. Another novelty is in application of
transductive methods from statistical learning theory to estimate the conditional error probabilities of the system.
This set of parameters is used to optimize the system performance.