i hope this is what i am searching for, i have been searching in this topic for almost 6 months and i could not find any thing to help me till now, the image processing field is too large and finding specific topics is hard. but i am glad i found this link, although i am commenting to see if this a fake or no
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matlab code for feature extraction from image for signature
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i hope this is what i am searching for, i have been searching in this topic for almost 6 months and i could not find any thing to help me till now, the image processing field is too large and finding specific topics is hard. but i am glad i found this link, although i am commenting to see if this a fake or no
19-04-2016, 11:43 AM
matlab code for feature extraction from image for signature
Abstract
Signature verification is one of the most accepted biometric techniques, because a signature is a part of everyday life, although less accurate than biometric techniques such as using the iris. In this field, much attention has been paid to features, because a verification system should be able to overcome problems such as forgeries, insensitivity to intra-personal variability and sensitivity to inter-personal variability. In this paper, we present a simple and efficient approach to on-line signature verification, based on a discrete cosine transform, which has been applied to 44 time signals, such as position, velocity, pressure and angle of pen. Experiments are carried out on two benchmark databases, SVC2004 and SUSIG. The forward feature selection algorithm is used to search for the best performing feature subsets. The proposed system is tested with different classifiers, with skilled forgery, and equal error rates were 3.61%, 2.04% and 1.49% for SVC2004 Task1&2, Task2 and SUSIG databases, respectively.
Introduction
Biometric verification techniques require a user to present identifying information based on an unchangeable personal feature. This may be a physical characteristic, such as a fingerprint or an iris, or it may be characteristic behavior, such as a signature or voice [1]. Signatures have been considered a typical form of authentication in our society for hundreds of years. Signature verification is the most natural and friendly approach in personal authentication for many biometric-based verification systems.
A signature is a simple, concrete expression of the unique variations in human hand geometry. The way a person signs his or her name is known to be characteristic of that individual. Signatures are learnt and acquired over a period of time rather than being a physiological characteristic, and are influenced by the physical and emotional conditions of a subject.
A signature verification system must be able to detect forgeries, and, at the same time, reduce rejection of genuine signatures. Analysis of signatures based on the method used to capture the signatures is divided into two main categories; off-line and on-line. In off-line verification, the signature patterns are signed on paper, and then scanned by plate-form scanners. On-line signature patterns possess more information than off-line patterns. There are not only static geometrical shapes but also dynamic writing information, such as speed, acceleration, and pressure, etc. On-line signature verification methods have proved to be more accurate than off-line methods
Signatures are subject to intra-personal variations. Hence, a signature verification system is feasible only if the system is insensitive to intra-personal variability, but sensitive to inter-personal variability. Even when insensitive to intra-personal variations, the system must possess the discriminating power to foil skilful forgers.
Significant research has been conducted in feature extraction and selection for the application of on-line signature verification and All these features may be important for some problems, but for a given task, only a small subset of features is relevant. In addition to a reduction in storage requirements and computational cost, these may also lead to an improvement in general performance. On the other hand, selection of a feature subset requires a multicriterion optimization function, e.g. the number of features and accuracy of classification.
Abstract
Signature verification is one of the most accepted biometric techniques, because a signature is a part of everyday life, although less accurate than biometric techniques such as using the iris. In this field, much attention has been paid to features, because a verification system should be able to overcome problems such as forgeries, insensitivity to intra-personal variability and sensitivity to inter-personal variability. In this paper, we present a simple and efficient approach to on-line signature verification, based on a discrete cosine transform, which has been applied to 44 time signals, such as position, velocity, pressure and angle of pen. Experiments are carried out on two benchmark databases, SVC2004 and SUSIG. The forward feature selection algorithm is used to search for the best performing feature subsets. The proposed system is tested with different classifiers, with skilled forgery, and equal error rates were 3.61%, 2.04% and 1.49% for SVC2004 Task1&2, Task2 and SUSIG databases, respectively.
Introduction
Biometric verification techniques require a user to present identifying information based on an unchangeable personal feature. This may be a physical characteristic, such as a fingerprint or an iris, or it may be characteristic behavior, such as a signature or voice [1]. Signatures have been considered a typical form of authentication in our society for hundreds of years. Signature verification is the most natural and friendly approach in personal authentication for many biometric-based verification systems.
A signature is a simple, concrete expression of the unique variations in human hand geometry. The way a person signs his or her name is known to be characteristic of that individual. Signatures are learnt and acquired over a period of time rather than being a physiological characteristic, and are influenced by the physical and emotional conditions of a subject.
A signature verification system must be able to detect forgeries, and, at the same time, reduce rejection of genuine signatures. Analysis of signatures based on the method used to capture the signatures is divided into two main categories; off-line and on-line. In off-line verification, the signature patterns are signed on paper, and then scanned by plate-form scanners. On-line signature patterns possess more information than off-line patterns. There are not only static geometrical shapes but also dynamic writing information, such as speed, acceleration, and pressure, etc. On-line signature verification methods have proved to be more accurate than off-line methods
Signatures are subject to intra-personal variations. Hence, a signature verification system is feasible only if the system is insensitive to intra-personal variability, but sensitive to inter-personal variability. Even when insensitive to intra-personal variations, the system must possess the discriminating power to foil skilful forgers.
Significant research has been conducted in feature extraction and selection for the application of on-line signature verification and All these features may be important for some problems, but for a given task, only a small subset of features is relevant. In addition to a reduction in storage requirements and computational cost, these may also lead to an improvement in general performance. On the other hand, selection of a feature subset requires a multicriterion optimization function, e.g. the number of features and accuracy of classification.
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