Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes

Dhieb, Thameur; Boubaker, Houcine; Ouarda, Wael; Njah, Sourour; Ayed, Mounir Ben; Alimi, Adel M.

doi:10.1007/s11042-020-10412-8

Cited by 14 publications

(7 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, to get the correct path of the signature in the air even if the viewpoint changes during the signing, we will incorporate the MediaPipe Hands framework [12] into our work, offering accurate finger and hand tracking capabilities. Secondly, in order to well characterize the in-air signature, we will use the Betaelliptical approach [13], [14] and the fuzzy perceptual detector [15], [16]. Finally, to tackle the issue of having a limited number of reference signatures for each signer, we will utilize the Dynamic Time Warping algorithm (DTW).…”

Section: B Contributionsmentioning

confidence: 99%

“…we are interested to use Beta-elliptical approach in the in-air signature verification system, which sufficiently represents the hand movements in real-time, by describing together its profile parts: the Beta impulses and the elliptic arcs [13], [14], [30]. Besides, we are interested to utilize the fuzzy perceptual detector in order to adequately represent the hand movements path [15], [16].…”

Section: Features Extractionmentioning

confidence: 99%

See 1 more Smart Citation

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Alobaidi,

Dhieb,

Hamdani

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Believing that biometrics trends are moving to distant and contactless mode, in-air signature verification is nowadays considered as one of the principal users biometric identification in contactless mode allowing users identification by drawing their handwritten signature in the air. In-air signature verification is used in many applications like access control and forensic analysis. In this regard, we propose a novel system for in-air signature verification using Beta stroke segmentation. The Beta-elliptical approach and the fuzzy perceptual detector are used for features extraction. The proposed system defines a specific data acquisition protocol and uses preprocessing steps to prepare data. Finally, the verification phase is done based on Dynamic Time Warping. To evaluate our proposed system, we have created two in-air signature datasets with and without the use of a transparent glass plate, which we make publicly available at https://ieeedataport.org/documents/air-signature-databases, termed respectively In-Air Signature dataset (IAS dataset) and In-Air Signature dataset using Glass Plate (IASGP dataset). Our verification system demonstrates promising outcomes, yielding an Equal Error Rate (EER) of 1.25% when applied to the IAS dataset and an EER of 2.00% when applied to the IASGP dataset in the skilled-forgery scenario. Extensive evaluations were conducted on both the 3DAirSig and the DeepAirSig datasets. The results confirm that the proposed system has a good performance compared to existing in-air signature verification systems for both skilled-forgery and random-forgery scenarios. INDEX TERMSIn-air signature verification; Beta-elliptical approach; fuzzy perceptual detector; Dynamic Time Warping.

show abstract

Section: B Contributionsmentioning

confidence: 99%

Section: Features Extractionmentioning

confidence: 99%

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Alobaidi,

Dhieb,

Hamdani

et al. 2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Beta-elliptic model for 2D writing movement consists in representing the trajectory by a combination of kinematic and geometric features (Bezine et al 2003;Kherallah et al 2004;Boubaker et al 2007). It is widely used in several areas of research on online handwriting such as writer identification (Dhieb et al 2021;Dhieb et al 2018) and handwriting recognition (Akouaydi et al 2019;Hamdi et al 2019;Hamdi et al 2017). The curvilinear velocity profile of the movement is reconstructed by overlapped Beta functions (Alimi 1997) while the geometric representation is based on elliptic shapes.…”

Section: The Beta-elliptic Modelmentioning

confidence: 99%

Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

Dhieb

Rezzoug

Boubaker

et al. 2021

Computer Methods in Biomechanics and Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

This paper investigates whether age, gender, and degree of familiarity with writing have an influence on the Beta-elliptic model errors during hand-drawing on a graphical tablet. A database of elliptical hand drawing movements was built within a sample of 99 participants aged between 19 and 85 years. Using the Betaelliptic model, the velocity profile was modeled by overlapped Beta functions and the drawing trajectory was segmented between velocity extrema and each segment geometry was modeled by elliptic arcs. Average absolute and relative geometric, curvature and curvilinear velocity errors were 0.27 mm, 0.68%, 4.54 mm, 0.48%, 4.68 mm/s, and 8.79% respectively. Statistical analyses revealed not significant or low correlation between modeling errors and age and movement velocity, and no significant or low error differences according to gender or degree of familiarity with writing.

show abstract

“…The Beta elliptic was performed in systems for writer identification and verification. [29], [30], [31] An online handwriting script can be segmented into segments and each segment is a group of strokes. In fact, the number of strokes is identified automatically from the curvilinear velocity representation.…”

Section: Segmentation Stepmentioning

confidence: 99%

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Akouaydi,

Njah,

Ouarda

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Due to the omnipresence of mobile devices, online handwritten scripts have become the most important feeding input to smartphones and tablet devices. To increase online handwriting recognition performance, deeper neural networks have extensively been used. In this context, our paper handles the problem of online handwritten script recognition based on extraction features system and deep approach system for sequences classification. Many solutions have appeared in order to facilitate the recognition of handwriting. Accordingly, we used an existent method and combined with new classifiers in order to get a flexible system. Good results are achieved compared to online characters and words recognition system on Latin and Arabic scripts. The performance of our two proposed systems is assessed by using five databases. Indeed, the recognition rate exceeds 98%.

show abstract

Deep bidirectional long short-term memory for online multilingual writer identification based on an extended Beta-elliptic model and fuzzy elementary perceptual codes

Cited by 14 publications

References 40 publications

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

In-Air Signature Verification System Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

Neural Architecture based on Fuzzy Perceptual Representation For Online Multilingual Handwriting Recognition

Contact Info

Product

Resources

About