Using brain prints as new biometric feature for human recognition

Aloui, Karam; Naït‐Ali, Amine; Naceur, Mohamed Saber

doi:10.1016/j.patrec.2017.10.001

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…Figure 1 shows the diagram of bio-keys generation, based on cardiocycles intervals (Aloui et al, 2018, p. 43). (Aloui, Nait-Ali, & Naceur, 2018) Paper (Krishnan et al, 2019) (Challa et al, 2018, p. 535).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Modern Medicine: Issues And Prospects

Bogdanov¹,

Горбунов²,

Zabikhullin³

et al. 2020

European Proceedings of Social and Behavioural Sciences

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Section: Methodsmentioning

confidence: 99%

“…With age, patients get more diseases, including chronic ones. The problem of postoperative and follow-up treatment becomes relevant (Aloui, Nait-Ali, & Naceur, 2018;Altop, Seymen, & Levi, 2019;Bhurane, Sharma, San-Tan, & Acharya, 2019;Chukwunonyerem et al, 2016;Koya & Deepthi, 2018).…”

Section: Problem Statementmentioning

confidence: 99%

Modern Medicine: Issues And Prospects

Bogdanov¹,

Горбунов²,

Zabikhullin³

et al. 2020

European Proceedings of Social and Behavioural Sciences

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“…Many studies and researches consider biometric features of brain fingerprints, the most common of them are discuss. Aloui et al (2018), the authors proposed extracting brain fingerprints based on cortical regions shown in MRI images of the brains of more than 200 people. These images were processed by 3D processing of cortical surfaces and converted into 2D maps, where the features were extracted using Wavelet Gabor Transform.…”

Section: Introductionmentioning

confidence: 99%

Human Identification Model Considering Biometrics Features

Radhi

2022

JLMA

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In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.

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“…However, though the face is easily the most identifiable feature, it is not the only feature of an MRI that can be used to identify individuals (Amico & Goñi, 2018;Byrge & Kennedy, 2019). The brain's anatomy is unique to each person: the folding pattern of the cortex is identifiable, just like a person's fingerprint, and could therefore be used to de-anonymise research participants if that brain fingerprint could be linked to another database (Aloui et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Sharing individualised template MRI data for MEG source reconstruction: a solution for open data while keeping subject confidentiality

Vinding

Oostenveld

2021

Preprint

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The increasing requirements for adoption of FAIR data management and sharing original research data from neuroimaging studies can be at odds with protecting the anonymity of the research participants due to the person-identifiable anatomical features in the data. We propose a solution to this dilemma for anatomical MRIs used in MEG source analysis. In MEG analysis, the channel-level data is reconstructed to the source-level using models derived from anatomical MRIs. Sharing data, therefore, requires sharing the anatomical MRI to replicate the analysis. The suggested solution is to replace the individual anatomical MRIs with individualised warped templates that can be used to carry out the MEG source analysis and that provide sufficient geometrical similarity to the original participants’ MRIs.First, we demonstrate how the individualised template warping can be implemented with one of the leading open-source neuroimaging analysis toolboxes. Second, we compare results from four different MEG source reconstruction methods performed with an individualised warped template to those using the participant’s original MRI. While the source reconstruction results are not numerically identical, there is a high similarity between the results for single dipole fits, dynamic imaging of coherent sources beamforming, and atlas-based virtual channel beamforming. There is a moderate similarity between minimum-norm estimates, as anticipated due to this method being anatomically constrained and dependent on the exact morphological features of the cortical sheet.We also compared the morphological features of the warped template to those of the original MRI. These showed a high similarity in grey matter volume and surface area, but a low similarity in the average cortical thickness and the mean folding index within cortical parcels.Taken together, this demonstrates that the results obtained by MEG source reconstruction can be preserved with the warped templates, whereas the anatomical and morphological fingerprint is sufficiently altered to protect the anonymity of research participants. In cases where participants consent to sharing anatomical MRI data, it remains preferable to share the original defaced data with an appropriate data use agreement. In cases where participants did not consent to share their MRIs, the individualised warped MRI template offers a good compromise in sharing data for reuse while retaining anonymity for those research participants.

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Using brain prints as new biometric feature for human recognition

Cited by 15 publications

References 34 publications

Modern Medicine: Issues And Prospects

Modern Medicine: Issues And Prospects

Human Identification Model Considering Biometrics Features

Sharing individualised template MRI data for MEG source reconstruction: a solution for open data while keeping subject confidentiality

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