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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23 Sep 2007
TL;DR: The principles behind context dependent modeling are presented and the reasons for its limited applicability in recognizing offline handwriting data are discussed, suggesting that it can not easily be exploited for offline recognition.
Abstract: The use of context dependent modeling units in handwriting recognition has been considered by many authors as promising substantial performance improvements in systems based on Hidden-Markov models. Interestingly, in the literature only a few approaches limited to online recognition are documented to make use of this technology. Therefore, we investigated whether context dependent modeling also offers advantages for offline recognition systems. The moderate performance improvements we achieved on a challenging unconstrained handwriting recognition task suggest that context dependent modeling can not easily be exploited for offline recognition. In this paper we will present the principles behind context dependent modeling and discuss the reasons for its limited applicability in recognizing offline handwriting data.
21 citations
15 Dec 2014
TL;DR: This paper proposes several approaches to the synthesis of off-line enhanced signatures from real dynamic information, showing a performance very similar to the one offered by real signatures, even increasing their discriminative power under the skilled forgeries scenario, one of the biggest challenges of handwriting recognition.
Abstract: One of the main challenges of off-line signature verification is the absence of large databases. A possible alternative to overcome this problem is the generation of fully synthetic signature databases, not subject to legal or privacy concerns. In this paper we propose several approaches to the synthesis of off-line enhanced signatures from real dynamic information. These synthetic samples show a performance very similar to the one offered by real signatures, even increasing their discriminative power under the skilled forgeries scenario, one of the biggest challenges of handwriting recognition. Furthermore, the feasibility of synthetically increasing the enrolment sets is analysed, showing promising results.
21 citations
07 Dec 2015
TL;DR: State-of-Art about both types of HSV Systems is presented, current methods used for features extraction and approaches used for verification in signature systems are presented.
Abstract: Recently, handwritten signature verification (HSV) has become atremendously active area of research. Considerable results have been achieved in terms of accuracy and computation so far. Generally, biometrics can be divided into two types:Behavioral (signature verification, keystroke dynamics, etc.) and Physiological (iris characteristics, fingerprint, etc.). Signature verification is widely studied and discussed by using two approaches, on-line and offline approaches. Offline systems are more applicable and easy to use in comparison with on-line systems in many parts of the world. However, it is considered more difficult than on-line verification due to the lack of dynamic information. This paper presents State-of-Art about both types of HSV Systems. In this paper, we present recent methods used to capture data as well as different methods and techniques used in pre-processing steps. Additionally, current methods used for features extraction and approaches used for verification in signature systems are presented. Finally, we discuss approaches as well as techniques that have been used. In conclusion, we recommend encouraging ideas to be incorporated in the future.
21 citations
01 Dec 2009
TL;DR: A new mobile multimodal biometric system based on the fusion of finger vein and fingerprint recognition that has enough processing power and storage for many biometric data by using a conventional ultra mobile personal computer (UMPC) as an embedded system.
Abstract: Mobile multimodal biometric systems have been recently used to overcome the limitations of unimodal biometric systems and to achieve high recognition accuracy. However, the conventional embedded mobile biometric systems have low processing power and small storage so they are insufficient for processing many biometric data. In addition, user feels inconvenience because they capture biometric data in several steps, which requires specific behaviors of the user. Therefore, we propose a new mobile multimodal biometric system based on the fusion of finger vein and fingerprint recognition. The recognition step can be completed within short time because the proposed system obtains finger vein and fingerprint images simultaneously. In addition, the proposed system has enough processing power and storage for many biometric data by using a conventional ultra mobile personal computer (UMPC) as an embedded system. Keywords-component; multimodal biometrics, finger vein, fingerprint
21 citations