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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
de Rosa, Gustavo H. [1, 2] ; Roder, Mateus [1] ; Papa, Joao P. [1]
Total Authors: 3
Affiliation:
[1] Sao Paulo State Univ, Dept Comp, Bauru, SP - Brazil
[2] Av Eng Luis Edmundo Carrijo Coube 14-01, Bauru, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: EXPERT SYSTEMS; NOV 2021.
Web of Science Citations: 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. (AU)

FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
Grantee:Fabio Gagliardi Cozman
Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 20/12101-0 - Support for computational environments and experiments execution: data acquisition, categorization and maintenance
Grantee:Leandro Aparecido Passos Junior
Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
FAPESP's process: 19/02205-5 - Adversarial learning in natural language processing
Grantee:Gustavo Henrique de Rosa
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC