Éditeur(s) : HAL CCSD
Résumé : International audience
Handwritten signatures are considered one of the most useful biometric traits for personal verification. In the networked society, in which a multitude of different devices can be used for signature acquisition, specific research is still needed to determine the extent to which features of an input signature depend on the characteristics of the signature apposition process. In this paper an experimental investigation was carried out on constrained signatures, which were acquired using writing boxes having different area and shape, and the different behaviour of dynamic features with respect to the writing boxes are discussed.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165894
https://hal.univ-antilles.fr/hal-01165894
https://hal.univ-antilles.fr/hal-01165894/document
https://hal.univ-antilles.fr/hal-01165894/file/IGS_2015_submission_23.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
This paper addresses the problem of word spotting in handwritten documents. The method is segmentation-free and follows the query-by-string paradigm. In the paper, we focus on the first step of the whole bio-inspired process that is based on two filtering steps, which are a global filtering followed by a more local filtering after a change of observation scale. The contribution of this approach is the use and the generalization of the Haar-Like-Features for the analysis of the document images, inspired from the famous visual perception principle. Different pieces of information are extracted from the whole image before drawing a conclusion, after a process of accumulation of votes. The method is evaluated using the IAM Handwriting Database.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165920
https://hal.univ-antilles.fr/hal-01165920
https://hal.univ-antilles.fr/hal-01165920/document
https://hal.univ-antilles.fr/hal-01165920/file/IGS_2015_submission_33.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
Signatures provide a convenient and widely accepted method of authentication, however they are prone to attack by forgery. This can be mitigated to an extent by analysing both the static and dynamic biometric aspects of construction, however the possibly for accurate forgery from a static image of a genuine signature still exists. In this study we explore initial forgery accuracy of a range of genuine signatures and how accuracy changes as a forger receives feedback from a commercial signature engine in terms of a 'match score'. We also explore the effects of genuine signature complexity on forgery performance alongside the image size of the genuine signature to be forged. Our results show that forgers are able improve performance over time on simple signatures (including those with less pen travel distance) and that a magnified genuine sample enables more accurate forgery for these class of signatures. More complex signatures result in lower forged verification scores and irregular patterns of improvement across the five forgeries. Overall verification match scores were typically less than 80% for most attempts regardless of signature complexity, thus indicating the resilience of dynamic systems to unskilled forgery attempts.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165763
https://hal.univ-antilles.fr/hal-01165763
https://hal.univ-antilles.fr/hal-01165763/document
https://hal.univ-antilles.fr/hal-01165763/file/IGS_2015_submission_1.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
This article proposes a new indicator for handwriting examination in text independent framework. Experiments of writer identification are addressed using only handwritten short lines instead of a whole character. After preprocessing such as binarization and contours extraction, profiles of contours of handwritten lines were decomposed with fifth scales wavelet decomposition. As the result, we obtained indicators which showed qualities of handwritten lines such as smooth or jaggy. The indicators were analysed with Principle Component Analysis (PCA), and eigen vectors were obtained. In a phase of writer identification, using Kernel Orthogonal Mutual Subspace Method (KOMSM), subspace was calculated by the eigen vectors. The result obtained through the experiments was not enough to satisfy. In future works, the proposed method will be applied to whole handwritten characters.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165761
https://hal.univ-antilles.fr/hal-01165761
https://hal.univ-antilles.fr/hal-01165761/document
https://hal.univ-antilles.fr/hal-01165761/file/IGS_2015_submission_2.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
We present a model of the spinal cord in controlling one degree-of-freedom arm movements. The model includes both neural and musculoskeletal functions in an integrated framework. The model has been implemented by an artificial neural network coupled with a computational model of muscle publicly available. The experimental results show that the model is able to regulate the position of the arm and to mediate reflex actions by integrating commands from CNS and signals from proprioceptors.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165882
https://hal.univ-antilles.fr/hal-01165882
https://hal.univ-antilles.fr/hal-01165882/document
https://hal.univ-antilles.fr/hal-01165882/file/IGS_2015_submission_17.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we introduce a framework for on-line learning of handwritten symbols from scratch. As such, learning suffers from missing data at the beginning of the learning process, in this paper we propose the use of Sigma-lognormal model to generate synthetic data. Our framework deals with a real-time use of the system, where the recognition of a single symbol cannot be postponed by the generation of synthetic data. We evaluate the use of our framework and Sigma-lognormal model by comparison of the recognition rate to a block-learning and learning without any synthetic data. Experimental results show that both of these contributions represent an enhancement to the on-line handwriting recognition, especially when starting from scratch.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165923
https://hal.univ-antilles.fr/hal-01165923
https://hal.univ-antilles.fr/hal-01165923/document
https://hal.univ-antilles.fr/hal-01165923/file/IGS_2015_submission_34.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we introduce the Intuidoc-Loustic Gestures DataBase (ILGDB), a new publicly available database of realistic pen-based gestures for evaluation of recognition systems in pen-enabled interfaces. ILGDB was collected in a real world context and in an immersive environment. As it contains a large number of unconstrained user-defined gestures, ILGDB offers a unique diversity of content that is likely to serve as a precious tool for benchmarking of gesture recognition systems. We report first baseline experimental results on the task of Writer-Dependent gesture recognition.
ICPR2012 - 21st International Conference on Pattern Recognition
Tsukuba, Japan

hal-00802340
https://hal.inria.fr/hal-00802340
https://hal.inria.fr/hal-00802340/document

Éditeur(s) : HAL CCSD
Résumé : International audience
Drawing and handwriting are communicational skills that are fundamental in geopolitical, ideological and technological evolutions of all time. drawingand handwriting are still useful in defining innovative applications in numerous fields. In this regard, researchers have to solve new problems like those related to the manner in which drawing and handwriting become an efficient way to command various connected objects; or to validate graphomotor skills as evident and objective sources of data useful in the study of human beings, their capabilities and their limits from birth to decline.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

Droits : http://creativecommons.org/licenses/by-nc-nd/
ISBN : 979-10-95177-01-2
hal-01166509
https://hal.univ-antilles.fr/hal-01166509
https://hal.univ-antilles.fr/hal-01166509/document
https://hal.univ-antilles.fr/hal-01166509/file/IGS2015proceedingsvHal-1.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
We present a novel approach towards signature recognition from spatio-temporal data. The data is obtained by recording gyroscope and accelerometer measurements from an embedded pen device. The idea of Universum learning was previously presented by Vapnik and recently popularized in machine learning community. It assumes that the decision boundary of a classifier lies close to data with high uncertainty. The quality of the final classifier strongly depends on a way how to choose the Universum data and also on the representation of original data. In our paper we use a novel approach of Universum learning to classify signature data, also we present our novel idea how to sample the Universum data. At last, we also find more effective representation of the signature data itself compared to the baseline method. These three novelties allow us to outperform previously published results by 4.89% / 5.58%.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165925
https://hal.univ-antilles.fr/hal-01165925
https://hal.univ-antilles.fr/hal-01165925/document
https://hal.univ-antilles.fr/hal-01165925/file/IGS_2015_submission_38.pdf