Volume 39, Issue 4 e12891
ORIGINAL ARTICLE

Neighbour-based bag-of-samplings for person identification through handwritten dynamics and convolutional neural networks

Gustavo H. de Rosa

Corresponding Author

Gustavo H. de Rosa

Department of Computing, São Paulo State University, Bauru, Brazil

Correspondence

Gustavo H. de Rosa, Department of Computing, São Paulo State University, Bauru, SP, Brazil.

Email: [email protected]

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Mateus Roder

Mateus Roder

Department of Computing, São Paulo State University, Bauru, Brazil

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João P. Papa

João P. Papa

Department of Computing, São Paulo State University, Bauru, Brazil

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First published: 29 November 2021

Funding information: Conselho Nacional de Desenvolvimento Científico e Tecnológico, Grant/Award Numbers: 307066/2017-7, 427968/2018-6; Fundação de Amparo à Pesquisa do Estado de São Paulo, Grant/Award Numbers: 2013/07375-0, 2014/12236-1, 2019/07665-4, 2019/02205-5, 2020/12101-0

Abstract

Biometric recognition provides straightforward methods to deal with the problem of identifying people under certain circumstances. Additionally, a well-calibrated biometric system enhances security policies and prevents malicious attempts, such as fraud or identity theft. Deep learning has arisen to foster the problem by extracting high-level features that compose the so-called ‘user fingerprint’, that is, digital identification of a particular individual. Nevertheless, personal identification is not a trivial task, as many traits might define an individual, varying according to the task's domain. An exciting way to overcome such a problem is to employ handwritten dynamics, which are hand- and motor-based signals from an individual's writing style and obtained through a biometric smartpen. In this work, we propose using such signals to identify an individual through convolutional neural networks. Essentially, the proposed work uses a neighbour-based bag-of-samplings procedure to sample the signals to a fixed size and feeds them into a neural network responsible for extracting their features and further classifying them. The experiments were conducted over two handwritten dynamic datasets, NewHandPD and SignRec, and established new fruitful state-of-the-art concerning these particular datasets and the corresponding context.

CONFLICT OF INTEREST

The authors declare no potential conflict of interests.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available in Recogna Laboratory at https://recogna.tech/files/datasets/signrec.rar.

The full text of this article hosted at iucr.org is unavailable due to technical difficulties.