A multimodal-Siamese Neural Network (mSNN) for person verification using signatures and EEG

Chakladar, Debashis Das; Kumar, Pradeep; Roy, Partha Pratim; Dogra, Debi Prosad; Scheme, Erik; Chang, Victor

doi:10.1016/j.inffus.2021.01.004

Cited by 34 publications

(9 citation statements)

References 46 publications

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“…In recent years, researchers have used SNNs on a combination of biometric features and signatures, or spatial features from a 3D writing system. [ 34,35 ]…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, researchers have used SNNs on a combination of biometric features and signatures, or spatial features from a 3D writing system. [34,35] Cryptographic signatures are strongly recommended as surrogates if they can provide the same legal enforcement in the region. Cryptographic digital signature offers technology for identity verification and content integrity protection, and solutions from open source to commercialization are all mature.…”

Section: Insight Of Signaturesmentioning

confidence: 99%

See 1 more Smart Citation

Is There a Difference between Paper and Electronic Chinese Signatures?

Luo,

Pu,

Yin

et al. 2023

Advanced Intelligent Systems

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The purpose of this study is to investigate whether there are differences in handwritten Chinese signatures on different media including paper and electronic devices. Participants were asked to sign specified names on various types of media and the signatures were scanned or saved digitally for subsequent analysis. In this study, using convolutional neural networks and Siamese neural networks as classifiers and comparators, the performance plunge is revealed and thus considerable dissimilarity between the signatures on different media is implied. To further explore this, cubic Bézier curves are fitted to the signatures using the least square method for quantitative statistical analysis. By analyzing the visual changes in the morphology of strokes, several features of signatures are selected and computed, and the paired t‐test and the Wilcoxon signed‐rank test are implemented, which provides a deeper substantiation and explanation of the findings.

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“…In recent years, researchers have used SNNs on a combination of biometric features and signatures, or spatial features from a 3D writing system. [ 34,35 ]…”

Section: Discussionmentioning

confidence: 99%

Section: Insight Of Signaturesmentioning

confidence: 99%

Is There a Difference between Paper and Electronic Chinese Signatures?

Luo,

Pu,

Yin

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…EEG equipment has long been used to detect neurological disorders such as epilepsy, while more recently fNIRS has demonstrated promise in the diagnosing of Alzheimer's Disease [52]. Additionally, since raw data collected from an individual via EEG and other BCI methods can be used to identify them between platforms with relative ease [13], ubiquitous adoption of BCIs poses significant risks if the associated BCI data is improperly managed and anonymized. Future security breaches leaking BCI data could affect an individual across multiple services and platforms.…”

Section: Ethical Implicationsmentioning

confidence: 99%

Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing

Davis

Spapé

Ruotsalo

2023

IEEE Trans. Affective Comput.

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We investigate inferring individual preferences and the contradiction of individual preferences with group preferences through direct measurement of the brain. We report an experiment where brain activity collected from 31 participants produced in response to viewing images is associated with their self-reported preferences. First, we show that brain responses present a graded response to preferences, and that brain responses alone can be used to train classifiers that reliably estimate preferences. Second, we show that brain responses reveal additional preference information that correlates with group preference, even when participants self-reported having no such preference. Our analysis of brain responses carries significant implications for researchers in general, as it suggests an individual's explicit preferences are not always aligned with the preferences inferred from their brain responses. These findings call into question the reliability of explicit and behavioral signals. They also imply that additional, multimodal sources of information may be necessary to infer reliable preference information.

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“…The core of SMSRFFN metric learning is to reduce the distance between samples of the same fault type and increase the distance between different fault types [37] by constructing a contrastive loss function. The contrastive loss function [38] is as follows:…”

Section: Smsrffn Loss Functionmentioning

confidence: 99%

Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition

Hou¹,

Wang²,

Lv³

et al. 2022

Meas. Sci. Technol.

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Implementing condition monitoring and fault diagnosis of aero-engine bearing is crucial for ensuring aircraft operate safely and reliably. In engineering practice, the fault data for aero-engine bearings is extremely limited. However, the traditional fault diagnosis methods have two shortcomings under extremely small sample conditions: (1) they have limited diagnostic performance and generalization ability; (2) they do not mine fault information sufficiently and efficiently. This article proposes a siamese multiscale residual feature fusion network (SMSRFFN) for aero-engine bearings fault diagnosis under small sample conditions to overcome the weaknesses above. In the proposed SMSRFFN, the training samples are first paired in pairs according to the matching rules to realize the expansion of the sample size. Secondly, a multiscale residual feature extraction network (MSRFEN) is constructed to excavate the fault features of different scales and speed up the convergence speed of the network. Then, a multiscale attention mechanism feature fusion module (MSAMFFM) is designed to achieve efficient fusion of fault features at different scales. Finally, the distance of the input sample is measured based on the fused deep feature representation to identify the fault state of the aero-engine bearing. The proposed SMSRFFN is evaluated using three bearing fault data and also compared with some state-of-the-art small sample diagnostic methods. Experimental results demonstrate the effectiveness and superiority of the proposed SMSRFFN in mining fault information and improving diagnosis accuracy under extremely small sample conditions.

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A multimodal-Siamese Neural Network (mSNN) for person verification using signatures and EEG

Cited by 34 publications

References 46 publications

Is There a Difference between Paper and Electronic Chinese Signatures?

Is There a Difference between Paper and Electronic Chinese Signatures?

Contradicted by the Brain: Predicting Individual and Group Preferences via Brain-Computer Interfacing

Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition

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