Signature barcodes for online verification. (April 2022)

• Record Type: Journal Article
• Authors: Alpar, Orcan
• Is Part Of: Pattern recognition. Volume 124(2022)
• Publication Date: 2022-04
• Subjects: Barcode -- Biometrics -- Online signature verification -- Scalogram -- Frequency -- SVM; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108426
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose the signature barcoding framework for the first time in literature. The barcode images carry information on the frequency component of the signature. Frequency vs. time localization is computed by CWT employing compatible wavelets. We tested the framework with 400 signatures from our dataset and SVC2004 samples. We managed to achieve 2.25% FAR, 2.75% FRR and 2.81% EER as the lowest rates. Abstract: As a sub-branch of behavioral biometrics, online signature verification systems deal with unique signing characteristics, which could be better differentiated by extraction of habitual singing styles instead of geometric features in case of perfect forgery. Even if the signatures are geometrically identical, speed and frequency components of the signing process might significantly vary. Therefore, a novel framework is introduced as a new signature verification protocol for touchscreen devices using barcodes containing the dominant frequency component of the speed signals. A special interface is designed as signature tracker to extract the displacement data sampled from the signing process. The speed signals are interpolated from the displacement data and the frequency components of the signals are computed by scalograms analysis governed by continuous wavelet transformations (CWT). The signature barcodes are generated as 4-scale scalograms and classified by support vector machines (SVM). Among several compatible wavelets, Gaussian derivative wavelet is selected for generating scalograms and the results of the process are calculated as 2.25% FAR, 2.75% FRR and 2.81%EER for our dataset. The framework is also tested with SVC2004 data that we achieved 0% FAR, 9.33% FRR and 8%EER, also with SUSIG-Visual, SUSIG-Blind, MOBISIG databases and we reached between 1.22%-3.62% average EERs, which are competitive among the relevant results. Given the promising outcomes, the signature barcoding is very reliable method which could be executed by a simple touchscreen interface collecting the barcodes for storing and benchmarking when needed.