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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 Jan 2003
TL;DR: The domain-dependent bilingual hand-printed character recognition system based on two important character properties, defined as spatial and temporal informative features, helps to solve ambiguity occurred in several Thai and English character, which conventional features cannot resolve.
Abstract: We propose a new method for recognition - the domain-dependent bilingual hand-printed character recognition. We implemented two-stage recognition systems based on two important character properties, defined as spatial and temporal informative features. The proposed spatial informative features (SIF) are off-line characters' structures that are exploited in order to differentiate Thai from English characters. These features can also be called distinctive features (DF). In contrast, temporal informative features (TIF) are segments of characters extracted using our proposed features, called start-to-end point distance feature, and other standard on-line features. Our proposed TIF features help us to solve ambiguity occurred in several Thai and English character, which conventional features cannot resolve. In the recognition system, the first stage is performed the language classification task using distinctive features, while the second stage is using hidden Markov model (HMM) as the final classifier. The advantages of using language classification at the first recognition stage are two folds. First, the decision complexity at the final recognition stage can be reduced. Second, the observation stages of two independent language HMMs can be better optimized than one bilingual HMM. From the experimental results, language classification recognition accuracy is 99.31%, while the recognition accuracy of Thai and English characters are 91.67% and 90.23%, respectively. Hence, the overall recognition accuracy is 91.05%.

4 citations

01 Jan 2009
TL;DR: A new model for on-line 3D signature verification using multiple parameters is proposed, highlighting the third dimensional distinctive parameter, depth of the signature for its hidden information.
Abstract: Summary Biometric based personal identification is a reliable and widely accepted method for authentication. Human 3D signature is distinct from other biometric authentication methods due to the presence of third dimensional hidden information. In this paper, a new model for on-line 3D signature verification using multiple parameters is proposed. The parameters considered from human signature namely, velocity, acceleration, pressure, direction, pen ups/downs, total time taken, length and depth of the signature are unique for each person. The proposed model highlights the third dimensional distinctive parameter, depth of the signature for its hidden information. Curve fitting is performed on the points obtained from different non-linearly spaced layers of the signature pad. The best fitted curves from all the layers and other signature parameters are used in the process of verification of 3D signature. The digital multi-parameters of the 3D signature are further encrypted with cryptographic algorithms to protect from cryptanalysis. The attempts for 3D signature expert forgery by satisfying both the global and local parameters of the signature are difficult. The application of the 3D signature verification broadly ranges from authentication of financial transactions to authorization of administrative documents.

4 citations

Book ChapterDOI
07 Jul 2009
TL;DR: This paper looks at individual recognition rates for both face and ear, and then at combined recognition rates, and shows that an automated multimodal biometric system achieves significant performance gains.
Abstract: In this paper, we present an automated multimodal biometric system for the detection and recognition of humans using face and ear as input. The system is totally automated, with a trained detection system for face and for ear. We look at individual recognition rates for both face and ear, and then at combined recognition rates, and show that an automated multimodal biometric system achieves significant performance gains. We also discuss methods of combining biometric input and the recognition rates that each achieves.

4 citations

Book ChapterDOI
10 Dec 2009
TL;DR: An efficient text-based directional signature recognition algorithm which verifies signatures, even when they are composed of special unconstrained cursive characters which are superimposed and embellished, is presented.
Abstract: Signatures continue to be an important biometric trait because it remains widely used primarily for authenticating the identity of human beings. This paper presents an efficient text-based directional signature recognition algorithm which verifies signatures, even when they are composed of special unconstrained cursive characters which are superimposed and embellished. This algorithm extends the character-based signature verification technique. The experiments carried out on the GPDS signature database and an additional database created from signatures captured using the ePadInk tablet, show that the approach is effective and efficient, with a positive verification rate of 94.95%.

4 citations


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