Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]

Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study i...

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Fecha de publicación:: 2020

Institución:: Universidad de Medellín

Repositorio:: Repositorio UDEM

Idioma:: spa

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network_acronym_str	REPOUDEM2
network_name_str	Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
title	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
spellingShingle	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio] Biometry Data fusion Information quality Signal processing
title_short	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
title_full	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
title_fullStr	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
title_full_unstemmed	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
title_sort	Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]
dc.subject.spa.fl_str_mv	Biometry Data fusion Information quality Signal processing
topic	Biometry Data fusion Information quality Signal processing
description	Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2021-02-05T14:58:05Z
dc.date.available.none.fl_str_mv	2021-02-05T14:58:05Z
dc.date.none.fl_str_mv	2020
dc.type.eng.fl_str_mv	Article
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dc.type.driver.none.fl_str_mv	info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv	16469895
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/11407/5936
identifier_str_mv	16469895
url	http://hdl.handle.net/11407/5936
dc.language.iso.none.fl_str_mv	spa
language	spa
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dc.relation.citationvolume.none.fl_str_mv	2020
dc.relation.citationissue.none.fl_str_mv	E27
dc.relation.citationstartpage.none.fl_str_mv	445
dc.relation.citationendpage.none.fl_str_mv	456
dc.relation.references.none.fl_str_mv	Al-Qazzaz, N., Hamid, S., Mohd, B., Ahmad, S., Escudero, J., (2018) Optimal EEG Channel Selection for Vascular Dementia Identification Using Improved Binary Gravitation Search Algorithm,”, pp. 125-130 Barra, S., Casanova, A., Fraschini, M., Nappi, M., Fusion of physiological measures for multimodal biometric systems (2017) Multimed. Tools Appl, 76 (4), pp. 4835-4847 Bouzouina, Y., (2017) Multimodal Biometric: Iris and Face Recognition Based on Feature Selection of Iris with GA and Scores Level Fusion with SVM Cai, H., Venkatasubramanian, K., Patient Identity Verification Based on Physiological Signal Fusion,” (2017) 2017 IEEE/ACM Int. Conf. Connect. Heal. Appl. Syst. Eng. Technol., pp. 90-95 Chan, H.-L., Kuo, P.-C., Cheng, C.-Y., Chen, Y.-S., Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition (2018) Front. Neuroinform., 12, p. 66 Chen, Y., A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes (2016) IEEE Trans. Inf. Forensics Secur., 11 (12), pp. 2635-2647 Das, R., Maiorana, E., Campisi, P., EEG Biometrics Using Visual Stimuli: A Longitudinal Study (2016) IEEE Signal Process. Lett., 23 (3), pp. 341-345 Di Martino, L., Fernández, T.A., Carbajal, G., Ruguay, M.O.U., (2014) Fusión biométrica D ICIEMBRE 2014 Resumen Duque-Mejía, C., Becerra, M.A., Zapata-Hernández, C., Mejia-Arboleda, C., Castro-Ospina, A.E., Delgado-Trejos, E., Peluffo-Ordóñez, D., Revelo-Fuelagán, J., Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study Intelligent Information and Database Systems. ACIIDS 2019, 11431. , Nguyen N., Gaol F., Hong TP., Trawiński B. (eds), Springer, Cham Essa, M.E.B., Elkhateb, A., Hassanien, A.E., Hamad, A., (2018) Cascade Multimodal Biometric System Using Fingerprint and Iris Patterns, 639 Falzon, O., Zerafa, R., Camilleri, T., Camilleri, K.P., EEG-based biometry using steady state visual evoked potentials (2017) Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 4159-4162 Kaewwit, C., Lursinsap, C., Sophatsathit, P., (2017) 373 High Accuracy Eeg Biometrics Identification Using Ica and Ar Model, 2 (2), pp. 354-373 Kaur, B., Singh, D., Neuro signals: A future biomertic approach towards user identification (2017) Proc. 7Th Int. Conf. Conflu. 2017 Cloud Comput. Data Sci. Eng., pp. 112-117 Kaur, B., Singh, D., (2017) Impact of Ageing on Biometric Systems: A Survey Impact of Ageing on Biometric Systems: A Survey, , no. March Kumar, K.K., Rao, P.T., (2018) “Information and Communication Technology for Intelligent Systems (ICTIS 2017), 83 Maiorana, E., la Rocca, D., Campisi, P., On the Permanence of EEG Signals for Biometric Recognition (2016) IEEE Trans. Inf. Forensics Secur., 11 (1), pp. 163-175. , Jan Mao, Z., Yao, W., Huang, Y., EEG-based biometric identification with deep learning (2017) Int. IEEE/EMBS Conf. Neural Eng. NER, pp. 609-612 Moreno-Revelo, M., Ortega-Adarme, M., Peluffo-Ordoñez, D.H., Alvarez-Uribe, K.C., Becerra, M.A., Comparison among physiological signals for biometric identification (2017) LNCS., 10585 Min, B.-K., Suk, H.-I., Ahn, M.-H., Lee, M.-H., Lee, S.-W., Individual Identification Using Cognitive Electroencephalographic Neurodynamics (2017) IEEE Trans. Inf. Forensics Secur., 12 (9), pp. 2159-2167 Mohamed, S., Haggag, S., Nahavandi, S., Haggag, O., Towards automated quality assessment measure for EEG signals (2017) Neurocomputing, 237, pp. 281-290. , https://doi.org/10.1016/J.NEUCOM.2017.01.002 Saini, R., Don’t just sign use brain too: A novel multimodal approach for user identification and verification (2018) Inf. Sci. (Ny), 430-431, pp. 163-178 Smith, R.J., Sugijoto, A., Rismanchi, N., Hussain, S.A., Shrey, D.W., Lopour, B.A., Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms (2017) Brain Topogr, 30 (6), pp. 810-821 Steinberg, A., Bowman, C., White, F., Revisions to the JDL Data Fusion (1991) Data Fusion Lexicon by JDL Torres-Valencia, C., Álvarez-López, M., Orozco-Gutiérrez, Á., SVM-based feature selection methods for emotion recognition from multimodal data (2017) J. Multimodal User Interfaces, 11 (1), pp. 9-23. , Mar Vahid, A., Arbabi, E., Human identification with EEG signals in different emotional states (2016) 2016 23Rd Iran. Conf. Biomed. Eng. 2016 1St Int. Iran. Conf. Biomed. Eng. ICBME, (November), pp. 242-246 Wu, Q., Zeng, Y., Lin, Z., Wang, X., Yan, B., (2017) Real-Time Eeg-Based Person Authentication System Using Face Rapid Serial Visual Presentation,”, pp. 564-567 Zapata, J.C., Duque, C.M., Rojas-Idarraga, Y., Gonzalez, M.E., Guzmán, J.A., Becerra Botero, M.A., Data fusion applied to biometric identification – A review (2017) Communications in Computer and Information Science, 735, pp. 721-733
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_16ec
rights_invalid_str_mv	http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv	Associacao Iberica de Sistemas e Tecnologias de Informacao
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Básicas
publisher.none.fl_str_mv	Associacao Iberica de Sistemas e Tecnologias de Informacao
dc.source.none.fl_str_mv	RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
institution	Universidad de Medellín
repository.name.fl_str_mv	Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv	repositorio@udem.edu.co
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spelling	20202021-02-05T14:58:05Z2021-02-05T14:58:05Z16469895http://hdl.handle.net/11407/5936Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.spaAssociacao Iberica de Sistemas e Tecnologias de InformacaoFacultad de Ciencias Básicashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85081033191&partnerID=40&md5=4f7e5a5fe31fb8c14e333340ce64d0ff2020E27445456Al-Qazzaz, N., Hamid, S., Mohd, B., Ahmad, S., Escudero, J., (2018) Optimal EEG Channel Selection for Vascular Dementia Identification Using Improved Binary Gravitation Search Algorithm,”, pp. 125-130Barra, S., Casanova, A., Fraschini, M., Nappi, M., Fusion of physiological measures for multimodal biometric systems (2017) Multimed. Tools Appl, 76 (4), pp. 4835-4847Bouzouina, Y., (2017) Multimodal Biometric: Iris and Face Recognition Based on Feature Selection of Iris with GA and Scores Level Fusion with SVMCai, H., Venkatasubramanian, K., Patient Identity Verification Based on Physiological Signal Fusion,” (2017) 2017 IEEE/ACM Int. Conf. Connect. Heal. Appl. Syst. Eng. Technol., pp. 90-95Chan, H.-L., Kuo, P.-C., Cheng, C.-Y., Chen, Y.-S., Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition (2018) Front. Neuroinform., 12, p. 66Chen, Y., A High-Security EEG-Based Login System with RSVP Stimuli and Dry Electrodes (2016) IEEE Trans. Inf. Forensics Secur., 11 (12), pp. 2635-2647Das, R., Maiorana, E., Campisi, P., EEG Biometrics Using Visual Stimuli: A Longitudinal Study (2016) IEEE Signal Process. Lett., 23 (3), pp. 341-345Di Martino, L., Fernández, T.A., Carbajal, G., Ruguay, M.O.U., (2014) Fusión biométrica D ICIEMBRE 2014 ResumenDuque-Mejía, C., Becerra, M.A., Zapata-Hernández, C., Mejia-Arboleda, C., Castro-Ospina, A.E., Delgado-Trejos, E., Peluffo-Ordóñez, D., Revelo-Fuelagán, J., Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study Intelligent Information and Database Systems. ACIIDS 2019, 11431. , Nguyen N., Gaol F., Hong TP., Trawiński B. (eds), Springer, ChamEssa, M.E.B., Elkhateb, A., Hassanien, A.E., Hamad, A., (2018) Cascade Multimodal Biometric System Using Fingerprint and Iris Patterns, 639Falzon, O., Zerafa, R., Camilleri, T., Camilleri, K.P., EEG-based biometry using steady state visual evoked potentials (2017) Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 4159-4162Kaewwit, C., Lursinsap, C., Sophatsathit, P., (2017) 373 High Accuracy Eeg Biometrics Identification Using Ica and Ar Model, 2 (2), pp. 354-373Kaur, B., Singh, D., Neuro signals: A future biomertic approach towards user identification (2017) Proc. 7Th Int. Conf. Conflu. 2017 Cloud Comput. Data Sci. Eng., pp. 112-117Kaur, B., Singh, D., (2017) Impact of Ageing on Biometric Systems: A Survey Impact of Ageing on Biometric Systems: A Survey, , no. MarchKumar, K.K., Rao, P.T., (2018) “Information and Communication Technology for Intelligent Systems (ICTIS 2017), 83Maiorana, E., la Rocca, D., Campisi, P., On the Permanence of EEG Signals for Biometric Recognition (2016) IEEE Trans. Inf. Forensics Secur., 11 (1), pp. 163-175. , JanMao, Z., Yao, W., Huang, Y., EEG-based biometric identification with deep learning (2017) Int. IEEE/EMBS Conf. Neural Eng. NER, pp. 609-612Moreno-Revelo, M., Ortega-Adarme, M., Peluffo-Ordoñez, D.H., Alvarez-Uribe, K.C., Becerra, M.A., Comparison among physiological signals for biometric identification (2017) LNCS., 10585Min, B.-K., Suk, H.-I., Ahn, M.-H., Lee, M.-H., Lee, S.-W., Individual Identification Using Cognitive Electroencephalographic Neurodynamics (2017) IEEE Trans. Inf. Forensics Secur., 12 (9), pp. 2159-2167Mohamed, S., Haggag, S., Nahavandi, S., Haggag, O., Towards automated quality assessment measure for EEG signals (2017) Neurocomputing, 237, pp. 281-290. , https://doi.org/10.1016/J.NEUCOM.2017.01.002Saini, R., Don’t just sign use brain too: A novel multimodal approach for user identification and verification (2018) Inf. Sci. (Ny), 430-431, pp. 163-178Smith, R.J., Sugijoto, A., Rismanchi, N., Hussain, S.A., Shrey, D.W., Lopour, B.A., Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms (2017) Brain Topogr, 30 (6), pp. 810-821Steinberg, A., Bowman, C., White, F., Revisions to the JDL Data Fusion (1991) Data Fusion Lexicon by JDLTorres-Valencia, C., Álvarez-López, M., Orozco-Gutiérrez, Á., SVM-based feature selection methods for emotion recognition from multimodal data (2017) J. Multimodal User Interfaces, 11 (1), pp. 9-23. , MarVahid, A., Arbabi, E., Human identification with EEG signals in different emotional states (2016) 2016 23Rd Iran. Conf. Biomed. Eng. 2016 1St Int. Iran. Conf. Biomed. Eng. ICBME, (November), pp. 242-246Wu, Q., Zeng, Y., Lin, Z., Wang, X., Yan, B., (2017) Real-Time Eeg-Based Person Authentication System Using Face Rapid Serial Visual Presentation,”, pp. 564-567Zapata, J.C., Duque, C.M., Rojas-Idarraga, Y., Gonzalez, M.E., Guzmán, J.A., Becerra Botero, M.A., Data fusion applied to biometric identification – A review (2017) Communications in Computer and Information Science, 735, pp. 721-733RISTI - Revista Iberica de Sistemas e Tecnologias de InformacaoBiometryData fusionInformation qualitySignal processingData fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]Articleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Becerra, M.A., Institución Universitaria Pascual Bravo, Medellín, 050042, Colombia, Universidad de Medellín, Medellín, 050026, ColombiaLasso-Arciniegas, L., Universidad Yachay Tech – SDAS Group, Urcuquí, 100115, EcuadorViveros, A., Universidad Yachay Tech – SDAS Group, Urcuquí, 100115, EcuadorSerna-Guarín, L., Instituto Tecnológico Metropolitano, Medellín, 050042, ColombiaPeluffo-Ordóñez, D., Universidad Yachay Tech – SDAS Group, Urcuquí, 100115, EcuadorTobón, C., Universidad de Medellín, Medellín, 050026, Colombiahttp://purl.org/coar/access_right/c_16ecBecerra M.A.Lasso-Arciniegas L.Viveros A.Serna-Guarín L.Peluffo-Ordóñez D.Tobón C.11407/5936oai:repository.udem.edu.co:11407/59362021-02-05 09:58:05.656Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

Data fusion and information quality for biometric identification from multimodal signals [Modelo jdl y calidad de la información para identificación biométrica a partir de señales multimodales: Estudio exploratorio]

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