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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TL;DR: In this article , the proposed approach intends to strengthen the security of biometric recognition frameworks by introducing two authentication modalities, which can be used to discriminate actual features from fake ones.
Abstract: Abstract: The automatic identification of people using their unique physical characteristics for security purposes is known as biometrics. Biometric authentication has a significant difficulty that calls for the development of more efficient methods in order to confirm the actual presence of a true legitimate trait as opposed to a fake self-manufactured synthetic or reconstructed sample. Recent developments in machine learning, computer vision, and pattern recognition have accelerated the development of the biometric recognition technology. The suggested approach intends to strengthen the security of biometric recognition frameworks by introducing two authentication. Each modality faces a unique set of difficulties. When practical, adults were used to evaluate the performance of technology and software created for infants. The type of biometric recognition technology will determine how accurate it is the effectiveness of the algorithm, the biometric trait used, and the calibre of the data collected. The recommended method beats earlier state-of-the-art approaches, and extra biometric data reveals extremely valuable information that may be used to quite efficiently discriminate actual features from fake ones
06 Jul 2015
TL;DR: To address the scalability problem of using shape context for signature matching, the proposed method speeds up the matching stage by representing the shape context features as a feature vector and then applies a clustering algorithm to assign signatures to their corresponding classes.
Abstract: Offline signature recognition is a very difficult task due to normal variability in signatures and the unavailability of dynamic information regarding the pen path. In this paper, a technique for signature recognition is proposed based on shape context that summarizes the global signature features in a rich local descriptor. The proposed system reaches 100i?ź% accuracy but had some scalability problems as a result of the correspondence problem between the queried signature and all the data set signatures. To address the scalability problem of using shape context for signature matching, the proposed method speeds up the matching stage by representing the shape context features as a feature vector and then applies a clustering algorithm to assign signatures to their corresponding classes.
01 Jan 2013
TL;DR: Blob shape recognition is used in various application fields such as industrial vision systems, parts recognition, positioning, inspection, etc based on numerical signature which is an effective image processing technique for shape recognition.
Abstract: Blob shape recognition is used in various application fields such as industrial vision systems, parts recognition, positioning, inspection, etc. This is based on numerical signature which is an effective image processing technique for shape recognition