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Signature recognition

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


Papers
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Book ChapterDOI
17 Sep 1997
TL;DR: In this article, an advanced real-time system for gesture recognition is presented, which is able to recognize complex dynamic gestures, such as hand waving, spin, pointing, and head moving.
Abstract: An advanced real-time system for gesture recognition is presented, which is able to recognize complex dynamic gestures, such as ”hand waving”, ”spin”, ”pointing”, and ”head moving”. The recognition is based on global motion features, extracted from each difference image of the image sequence. The system uses Hidden Markov Models (HMMs) as statistical classifier. These HMMs are trained on a database of 24 isolated gestures, performed by 14 different people. With the use of global motion features, a recognition rate of 92.9% is achieved for a person and background independent recognition.

63 citations

Journal ArticleDOI
TL;DR: An online handwritten character recognition algorithm, suitable for the home use computer, was developed, which recognizes a quickly written multi-stroke character using dynamic programming based pattern matching technique.
Abstract: An online handwritten character recognition algorithm, suitable for the home use computer, was developed. A character, written on a digitizing tablet, is expressed as a directional angle sequence. In order to recognize a quickly written multi-stroke character, the character pattern is converted into a single interconnected stroke pattern. The recognition is carried out using dynamic programming based pattern matching technique.

63 citations

Journal ArticleDOI
TL;DR: This work builds on the well known dynamic time warping framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence.
Abstract: Continuous action recognition is more challenging than isolated recognition because classification and segmentation must be simultaneously carried out. We build on the well known dynamic time warping framework and devise a novel visual alignment technique, namely dynamic frame warping (DFW), which performs isolated recognition based on per-frame representation of videos, and on aligning a test sequence with a model sequence. Moreover, we propose two extensions which enable to perform recognition concomitant with segmentation, namely one-pass DFW and two-pass DFW. These two methods have their roots in the domain of continuous recognition of speech and, to the best of our knowledge, their extension to continuous visual action recognition has been overlooked. We test and illustrate the proposed techniques with a recently released dataset (RAVEL) and with two public-domain datasets widely used in action recognition (Hollywood-1 and Hollywood-2). We also compare the performances of the proposed isolated and continuous recognition algorithms with several recently published methods.

62 citations

Proceedings ArticleDOI
24 Sep 2012
TL;DR: A new cancelable biometric template generation algorithm is developed using random projection and transformation-based feature extraction and selection and validated on multi-modal face and ear database.
Abstract: Multimodal biometric systems have emerged as highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. However, one major issue pertinent to unimodal system remains. It has to do with actual biometric characteristics of users being permanent, and their number being limited. Thus, if user's biometric is compromised, it might be impossible or highly difficult to replace it in a particular system. Cancellable biometric for individual biometric has been a significantly understudied problem. The concept of cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that users can change their single biometric template in a biometric security system. However, cancelability in multimodal biometric has been barely addressed at all. In this paper, we tackle the problem and present a novel solution for cancelable biometrics in multimodal system. We develop a new cancelable biometric template generation algorithm using random projection and transformation-based feature extraction and selection. Performance of the proposed algorithm is validated on multi-modal face and ear database.

62 citations

Journal ArticleDOI
TL;DR: A novel feature of the technique is its ability to refine the correspondence relation of the handwriting during model building and signature verification, which enables the verification and partial correction of segmentation errors to reduce the number of false rejections while maintaining the system security level and keeping thenumber of reference samples manageable.

61 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202310
202219
202122
202028
201925
201832