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 Mar 2010
TL;DR: This project suggesting a modified DTW algorithm with the proposed Missed Nodes Recovery Algorithm aims to improve the mapping performance, hence the development of stroke to stroke signature comparison is possible.
Abstract: On-line Signature Verification is a field of verifying the time series signature data that normally obtained from the tablet-based device. Unlike common signature image, the on-line signature image data consists of points that are arranged in sequence based on time. The aim of this research is to develop a new approach to map the strokes in both test and reference signatures as well as to verify the originality of the test signatures. Current methods make use of the DTW algorithm and its variant to segment them before comparing each of its data dimension. This project suggesting a modified DTW algorithm with the proposed Missed Nodes Recovery Algorithm aims to improve the mapping performance, hence the development of stroke to stroke signature comparison is possible. This project is also proposing a method to compare the strokes with its similar strokes in on-line signature. All algorithm and experiments will be carried out using Matlab. The output of this research project is an algorithm that can be used to map strokes as well as to compare similarity of strokes in on-line signature.
1 citations
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TL;DR: Applications of pattern recognition include character recognition, target detection, medical diagnosis, analysis of biomedical signals and images, remote sensing, identification of human faces and fingerprints, reliability, speech recognition and understanding, and machine parts recognition.
Abstract: During the past fifteen years, there has been a considerable growth of interest in problems of pattern recognition. This interest has created an increasing need for theoretical methods and experimental software and hardware for use in the design of pattern recognition systems. A number of books have been published on this subject,1-16and some special pattern recognition machines have been designed and built for practical use. Applications of pattern recognition include character recognition,12target detection, medical diagnosis, analysis of biomedical signals and images, remote sensing, identification of human faces and fingerprints, reliability,17socio-economics,18speech recognition and understanding,19and machine parts recognition.
1 citations
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TL;DR: A Fourier domain asymmetric cryptosystem for multimodal biometric security that is privacy protected since the encryption keys are provided by the human, and hence those are private keys.
Abstract: We propose a Fourier domain asymmetric cryptosystem for multimodal biometric security. One modality of biometrics (such as face) is used as the plaintext, which is encrypted by another modality of biometrics (such as fingerprint). A private key is synthesized from the encrypted biometric signature by complex spatial Fourier processing. The encrypted biometric signature is further encrypted by other biometric modalities, and the corresponding private keys are synthesized. The resulting biometric signature is privacy protected since the encryption keys are provided by the human, and hence those are private keys. Moreover, the decryption keys are synthesized using those private encryption keys. The encrypted signatures are decrypted using the synthesized private keys and inverse complex spatial Fourier processing. Computer simulations demonstrate the feasibility of the technique proposed.
1 citations
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01 Nov 2007
TL;DR: A new method to combine HMM and BPNN to decrease the dimension of input vector and still keep a high recognition rate while recognizing is presented.
Abstract: This paper uses BP neural network for modeling and recognition in the ASR (automatic speech recognition system) to get a high performance. But it still has some disadvantages, one of which is that it needs to construct a high dimension of input vector, so it will waste a lot of memory storage and spend much time in computing. In this paper we present a new method to combine HMM and BPNN to decrease the dimension of input vector and still keep a high recognition rate while recognizing.
1 citations
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27 Jul 1999TL;DR: The developed autoregressive hidden Markov model and introduced speech character vector provide a very high recognition performance in the isolated words recognition task.
Abstract: The purpose of this paper is to consider autoregressive hidden Markov models for the isolated words recognition task. The training and recognition algorithms for autoregressive hidden Markov models were developed and investigated. The speech feature vector was designed based on the perceptual psychoacoustical principles and arithmetic Fourier transform. The speech data base consisted from 200 belarussian words was created and used for experiments. The developed autoregressive hidden Markov model and introduced speech character vector provide a very high recognition performance.
1 citations