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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09 Oct 2009
TL;DR: A new offline signature verification system is presented, which considers a new combination of previously used features and introduces two new distance-based ones and a new feature grouping.
Abstract: Automatic online and offline signature recognition and verification is becoming ubiquitous in person identification and authentication problems, in various domains requiring different levels of security. There has recently been an increasing interest in developing such systems, with several views on which are the best discriminator features. This paper presents a new offline signature verification system, which considers a new combination of previously used features and introduces two new distance-based ones. A new feature grouping is presented. We have experimented with two classification methods and two feature selection techniques. The best performance so far was obtained with the Naive Bayes classifier on the reduced feature set (through feature selection).
12 citations
10 May 2010
TL;DR: This paper proposes a two-stage approach for personal identification using linear discriminant analysis to reduce the dimensionality of the feature space while maintaining discrimination between user classes and tailoring a probabilistic neural network for effective classification purposes.
Abstract: The 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.
12 citations
16 Aug 1998
TL;DR: This method provides a solution to the crucial issue of assigning reliable cost to the edges of the segmentation graph in the popular over-segmentation followed by dynamic programming approach for word recognition.
Abstract: We present a method of combining multiple classifiers for optimizing word recognition. The proposed method combines the results of individual classifiers in such a way that the correct word is more likely to be hypothesized. This method provides a solution to the crucial issue of assigning reliable cost to the edges of the segmentation graph in the popular over-segmentation followed by dynamic programming approach for word recognition. Three combination functions are proposed and implemented. Experiments show that proposed method has a significant improvement on the word recognition accuracy.
12 citations
03 Mar 2016
TL;DR: This work explores crowdsourcing for the establishment of human baseline performance on signature recognition according to three different scenarios in which laymen, people without Forensic Document Examiner experience, have to decide about the authenticity of a given signature.
Abstract: This work explores crowdsourcing for the establishment of human baseline performance on signature recognition. We present five experiments according to three different scenarios in which laymen, people without Forensic Document Examiner experience, have to decide about the authenticity of a given signature. The scenarios include single comparisons between one genuine sample and one unlabeled sample based on image, video or time sequences and comparisons with multiple training and test sets. The human performance obtained varies from 7% to 80% depending of the scenario and the results suggest the large potential of these collaborative platforms and encourage to further research on this area.
12 citations
06 Jul 2014
TL;DR: The proposed real-time dynamic hand gesture recognition system based on Hidden Markov Models with incremental learning method (IL-HMMs) to provide natural human-computer interaction can obtain better recognition rates.
Abstract: This paper proposes a real-time dynamic hand gesture recognition system based on Hidden Markov Models with incremental learning method (IL-HMMs) to provide natural human-computer interaction. The system is divided into four parts: hand detecting and tracking, feature extraction and vector quantization, HMMs training and hand gesture recognition, incremental learning. After quantized hand gesture vector being recognized by HMMs, incremental learning method is adopted to modify the parameters of corresponding recognized model to make itself more adaptable to the coming new gestures. Experiment results show that comparing with traditional one, the proposed system can obtain better recognition rates.
12 citations