Signature recognition

About: Signature recognition is a research topic. Over the lifetime, 2138 publications have been published within this topic receiving 37605 citations.

Improved segmentation through dynamic time warping for signature verification using a neural network classifier

Abstract: A segmentation technique based on dynamic time warping is investigated as a means of obtaining improved alignment between multiple signatures from a writer; the segments generated through this approach are represented by an autoregressive model. The signature verification performance is evaluated with a neural network based classifier, and shown to be a significant improvement over previous results obtained by the authors that were based on uniform segmentation.

HMM-based handwritten symbol recognition using on-line and off-line features

Abstract: This paper addresses the problem of recognizing on-line sampled handwritten symbols. Within the proposed symbol recognition system based on hidden Markov models different kinds of feature extraction algorithms are used analysing on-line features as well as off-line features and combining the classification results. By conducting writer-dependent recognition experiments, it is demonstrated that the recognition rates as well as the reliability of the results is improved by using the proposed recognition system. Furthermore, by applying handwriting data not representing symbols out of the given alphabet, an increase of their rejection rate is obtained.

Tree pattern system and method for multiple virus signature recognition

Abstract: A system, method and computer program product are provided for recognizing virus signatures. Initially, a list of virus signatures is provided. Next, the list of virus signatures is combined into a tree of virus signatures. Data is subsequently compared against the tree of virus signatures for virus signature recognition.

The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

Abstract: The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.

On-line signature verification with two-stage statistical models

Abstract: Signature verification is a challenging task, because only a small set of genuine samples can be acquired and usually no forgeries are available in real application. In this paper, we propose a new two-stage statistical system for automatic on-line signature verification. Our system is composed of a simplified GMM model for global signature features, and a discrete HMM model for local signature features. To be practical, we introduce specific simplification strategies for model building and training. Our system requires only 5 genuine samples for new users and relies on only 3 global parameters for quick and efficient system tuning. Experiments are conducted to verify the effectiveness of our system.