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Signature recognition

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


Papers
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Proceedings ArticleDOI
01 Dec 2015
TL;DR: A system that allows recognizing a person's emotional state with the help of recording audio signals and makes use of hybrid of HMM and SVM classifiers to get best results is proposed.
Abstract: This paper proposes a system that allows recognizing a person's emotional state with the help of recording audio signals. This system is able to recognize four emotions (anger, happiness, sadness and neutral) This emotion recognition technique is mainly composed of two subsystems as - 1) gender recognition (GR) and 2) emotion recognition (ER). It has been proved experimentally that the performance of emotion recognition increases because of the apriori knowledge about gender of the speaker. Traditional approach shows that selection of proper and unique features of speech signals reduces the unnecessary calculation complexity. Recently use of combination of two or more different classifiers is emerging trend in the classification field. As HMM is the best training algorithm and SVM is the best classification algorithm, proposed technique makes use of hybrid of HMM and SVM classifiers to get best results.

10 citations

01 Jan 2015
TL;DR: This paper presents the signature verification on Punjabi database of 50 persons based on HMM classifier and the experimental results shows the verification accuracy rate of 97%.
Abstract: Handwritten signature broadly used biometric which include elevated intrapersonal variance .Signature are generally used as the personal identification apparatus for human that the necessitate for verification system. Two types verification is performed generally online (dynamic) and offline signature verification (static). The static is offline technology that is used for documents authentication, the dynamic is online technology for signal processing and pattern recognition. The main motive of handwritten signature verification is to reduce fraud in financial transactions, security in crossing the international borders and boarding an aircraft. In this paper present the signature verification on Punjabi database of 50 persons. The features are extracted using the Gabor filter and matching is performed using SURF features and critical point matching. The classification is based on HMM classifier and the experimental results shows the verification accuracy rate of 97%. General terms: Biometrics, Signature verification, signature matching.

10 citations

Journal ArticleDOI
TL;DR: This research experiment ventures to find a solution for automating the framed signature recognition using a ballpoint pen and the extracted non-scale variant and scale variant features are used in a support vector machine in signature recognition algorithm.
Abstract: This research experiment ventures to find a solution for automating the framed signature recognition. Here signatures are made on a given frame using a ballpoint pen with a tip size of 0.5. Instead of direct neural networks based algorithm implementation, the extracted non-scale variant and scale variant features are used in a support vector machine in signature recognition algorithm. The outcome of the research appears as a GUI. The final outcome was 100% random signature isolation with over 88% trained forgery rejection. If not for 4 vulnerable signatures, this rate goes over 96% however the research carried out with worst environmental conditions and with least number of features. Thus, the results can be definitely improvable with modifications. 

10 citations

Journal ArticleDOI
TL;DR: An online handwritten signature verification system, in which a signature is modelled by an analytical approach based on the empirical mode decomposition, shows the importance of the adopted method and allows obtaining an equal error rate.
Abstract: The handwritten signature is a biometric method used to verify a person's identity. This study lies within the scope of an online handwritten signature verification system, in which a signature is modelled by an analytical approach based on the empirical mode decomposition. The organised system is tested on the SVC2004 task1 and MYCT-100 databases. The implemented evaluation protocol shows the importance of the adopted method and allows obtaining an equal error rate of 1.83 and 2.23% for the SVC2004 task1 and the MYCT-100 databases, respectively.

10 citations

Proceedings ArticleDOI
26 Feb 2010
TL;DR: A method for off-line signature recognition is presented that uses morphological dilation on signature template for measurement of the pixel variance and hence the inter class and intra class variations in the signature.
Abstract: Handwritten signatures are one of the oldest biometric traits for human authorization and authentication of documents. Majority of commercial application area deal with static form of signature. In this paper we present a method for off-line signature recognition. We have used morphological dilation on signature template for measurement of the pixel variance and hence the inter class and intra class variations in the signature. The proposed feature extraction mechanism is fast enough so that it can be applied for on-line signature verification also.

10 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202219
202122
202028
201925
201832