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 Jan 2013
TL;DR: An approach for signature is an offline environment based on Maximally Stable Extremely Regions (MSER) features based on geometric and MSER based feature which provides efficient recognition for offline signature.
Abstract: This paper describes an approach for signature is an offline environment based on Maximally Stable Extremely Regions (MSER) features. MSER features are the parts of the image where local binarization is stable over a large range of thresholds. We discuss a system designed using geometric and MSER based feature which provides efficient recognition for offline signature.
17 Nov 2009
TL;DR: A novel 3D face representation algorithm is proposed to reduce the number of vertices and optimize its computation time while maintaining reasonable recognition performance and experimental results indicated that the proposed algorithm is robust for biometric user authentication and is also reasonably fast for real-time processing.
Abstract: In this paper, we developed a biometric user authentication system based on 3-dimensional (3D) face recognition under ubiquitous computing environment. Since 2D based face recognition has been shown its structural limitation, 3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since 3D face model consists of a large number of vertices, 3D model based face recognition system is generally inefficient for real-time computation. We propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time while maintaining reasonable recognition performance. We evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-vision based 3D face capturing device. Additionally, various decision making similarity measures were explored for recognition performance. Our experimental results indicated that our proposed algorithm is robust for biometric user authentication and is also reasonably fast for real-time processing.
08 Oct 2015
TL;DR: A prototype, an offline signature verification system that extracts seven features of a given signature and compares them with the genuine signature contained in the database to see if a particular input signature is of that person or not is proposed.
Abstract: On a daily basis we use signature to authenticate and verify a person's identification. This emphasizes the need for an automatic verification system. The biggest problem in the offline signature verification is the acute lack in representation of signature on the basis of the shape and other features. The main problem is the unpredictability of the shapes of the region of interest i.e. the signature, which is related to the identity of a person. To authenticate a transaction a person's signature is taken as a validation proof. We are proposing a prototype, an offline signature verification system that extracts seven features of a given signature and compares them with the genuine signature contained in the database to see if a particular input signature is of that person or not.
14 Jul 2009
TL;DR: This paper presents a protection scheme based on a user dependent pseudo-random ordering of the DCT template coefficients that lets to increase the biometric recognition performance, because a hacker can hardly match a fake biometric sample without knowing the pseudo- random ordering.
Abstract: Biometric template security and privacy is a great concern of biometric systems, because unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. In this paper we present a protection scheme based on a user dependent pseudo-random ordering of the DCT template coefficients. In addition to privacy enhancement, this scheme also lets to increase the biometric recognition performance, because a hacker can hardly match a fake biometric sample without knowing the pseudo-random ordering.
TL;DR: In this article, a subspace recognition procedure based on the problem of subset selection is proposed to reconcile the inherent dimensional mismatch between finite element models and incomplete measured modal data by use of a sub space recognition procedure, where a signature is defined as the significant vector basis spanning the reduced residual matrix as defined by a singular value decomposition process.
Abstract: In damage detection algorithms the use of mode-force error arising from reduced analytical system matrices precludes the possibility of stiffness damage localization for those elements residing entirely outside the analysis set. Subsequently, any indication of mode-force error arising from damage appears as a force imbalance in each reduced degree of freedom due to the erroneous load paths introduced in the reduction process. The result is a smearing of the otherwise localized nature of the error thereby altering the interpretation of perturbation matrix-based damage detection results. To address this issue this work seeks to reconcile the inherent dimensional mismatch between finite element models and incomplete measured modal data by use of a subspace recognition procedure based on the problem of subset selection. The spatial characteristics of the reduced dynamic residual are assumed to represent a characteristic signature of a damaged element or set of elements where a signature is defined as the significant vector basis spanning the reduced residual matrix as defined by a singular value decomposition process. Stiffness damage is thereby localized by a measure of the subspace intersection dimension of the experimentally measured signature with analytically regenerated candidate elemental signatures. The analytically regenerated signatures arise from a mapping of elemental stiffness matrix sets via the transformation procedure used in the reduction process and the measured modal matrix. Multiple signatures are identified after an orthogonalization of the original target signature with respect to the most consistent signature in the previous iteration. For damage localization to be possible each mapping must project a nonnull and unique signature in the analysis set residual. This work is evaluated on damage simulations of a 155 mm aeroshell and the NASA Langley Research Center eight-bay experimental test bed.