1,721,024 research outputs found
A Novel Approach for Online Signature Verification Using Fisher Based Probabilistic Neural Network
No full textThe rapid advancements in communication, networking and mobility have entailed an urgency to further develop basic biometric capabilities to face security challenges. Online signature authentication is increasingly gaining interest thanks to the advent of high quality signature devices. In this paper, we propose a new approach for automatic authentication using dynamic signature. The key features consist in using a powerful combination of linear discriminant analysis (LDA) and probabibilistic neural network (PNN) model together with an appropriate decision making process. LDA is used to reduce the dimensionality of the feature space while maintining discrimination between users. Based on its results, a PNN model is constructed and used for matching purposes. Then a decision making process relying on an appropriate decision rule is performed to accept or reject a claimed identity. Data sets from SVC 2004 have been used to assess the performance of the proposed system. The results show that the proposed method competes with and even outperforms existing methods
A Novel Approach for Online Signature Verification Using Fisher Based Probabilistic Neural Network
No full textThe rapid advancements in communication, networking and mobility have entailed an urgency to further develop basic biometric capabilities to face security challenges.
Online signature authentication is increasingly gaining interest thanks to the advent of high quality signature devices. In this paper, we propose a new approach for automatic authentication using dynamic signature. The key features consist in using a powerful combination of linear discriminant analysis (LDA) and probabibilistic neural network (PNN)
model together with an appropriate decision making process. LDA is used to reduce the dimensionality of the feature space while maintining discrimination between users. Based on its results, a PNN model is constructed and used for matching purposes. Then a decision making process relying on an appropriate decision rule is performed to accept or reject a
claimed identity. Data sets from SVC 2004 have been used to assess the performance of the proposed system. The results show that the proposed method competes with and even
outperforms existing methods
An Effective Feature Selection Method for On-line Signature based Authentication
No full textIn this paper, we tackle the problem of identifying the relevant set of features that helps achieving accurate on-line signature based authentication. There exists a large set of features that can be acquired from the original signal or derived from it. Taking into account the whole set of features in the authentication process is time consuming. Furthermore, not all features are relevant and some of them are redundant. Consequently, finding the minimal set of relevant features is a prerequisite to perform fast authentication while achieving better accuracy. This feature selection task is combinatorial in nature. In our work, we handle it using a Discrete Quantum behaved Particle Swarm Optimization strategy (DQPSO). The space of possible feature sets is explored according to a QPSO dynamic where each set is encoded in terms of a binary representation. Data sets from SVC 2004 data base have been used in our experiments. Very encouraging results have been obtained
Combining Fisher Discriminant Analysis and Probabilistic Neural Network for Effective On-line Signature Recognition
No full textThe advent of new technologies enables capturing the dynamic of a signature. This has opened a new perspective for the possible use of signatures as a basis for an authentication system that is accurate and trustworthy enough to be integrated in practical applications. Automatic online signature recognition and verification is one of the biometric techniques being the subject of a growing and intensive research activity. In this paper. we address this problem and we propose a two-stage approach for personal identification. The first stage consists in the use of linear discriminant analysis to reduce the dimensionality of the feature space while maintaining discrimination between user classes. The second stage consists in tailoring a probabilistic neural network for effective classification purposes. Several experiments have been conducted using SVC2004 database. Very high classification rates have been achieved showing the effectiveness of the proposed approach
Robust Point Correspondence for Image Registration Using Optimization with Extremal Dynamics
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