Analysis of the Dynamics of Handwriting for Biometric Personality Identification Based on Cellular Automata

Bilan, Stepan; Bilan, Mykola; Bilan, Andrii; Bilan, Sergii

doi:10.1007/978-3-030-48378-4_4

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…A more correlated work has been recently proposed by Bilan et al (2021), where they have used the direction of movement from handwritten text to diminish the false recognition rates from statistical handwritten text images. Additionally, they combined the proposed approach with asynchronous cellular automata, which helped identify whether a person has written text in different directions and scales.…”

Section: Related Workmentioning

confidence: 99%

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

2021

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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.

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Section: Related Workmentioning

confidence: 99%

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

2021

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Multivaluedness in Cellular Automata with Strong Anticipation and Some Prospects for Computation Theory

Makarenko¹

2020

Wseas Transactions on Information Science and Applications

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It is considered the general formulations and properties of cellular automata with cells which have the strong anticipatory property (introduced by D. Dubois). Multivalued behavior (hyperincursion) of solutions of such CA is described. It was posed new research problems of computation theory related to presumable multivaluedness of cellular automata with strong anticipation property. Extending of classical automata, Turing machine and algorithms had been proposed. Also some relation of such cellular automata and quantum mechanics are discussed.

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Analysis of the Dynamics of Handwriting for Biometric Personality Identification Based on Cellular Automata

Cited by 2 publications

References 8 publications

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

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

Multivaluedness in Cellular Automata with Strong Anticipation and Some Prospects for Computation Theory

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