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

Abstract

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.

Parkinson’s Disease Detection From Online Handwriting Based on Beta-Elliptical Approach and Fuzzy Perceptual Detector

“…Indeed, the Beta-elliptical approach and the fuzzy perceptual detector are used for features extraction in various tasks like writer identification [34], [35], signature verification [36], [37] and character recognition [38]. In fact, the Betaelliptical approach is based on a description combining two aspects: 1) Beta functions that characterize the velocity profile in the dynamic domain.…”