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Título: | Biometric and Emotion Identification: An ECG Compression Based Method | Autor: | Brás, Susana Ferreira, Jacqueline H. T. Soares, Sandra C. Pinho, Armando J. |
Palavras-chave: | biometrics; emotion; quantization; data compression; Kolmogorov complexity | Data: | 2018 | Editora: | Frontiers Media S.A. | Projeto: | This work was supported by the European Regional Development Fund (FEDER), and FSE through the COMPETE2020 programme and by the Portuguese Government through FCT—Foundation for Science and Technology, in the scope of the projects UID/CEC/00127/2013 (IEETA/UA), CMUP-ERI/FIA/0031/2013 (VR2Market), PTDC/EEISII/ 6608/2014, and UID/IC/4255/2013 (CINTESIS, supported by FEDER through the operation POCI-01-0145-FEDER- 007746 funded by the Programa Operacional Competitividade e Internacionalizao COMPETE2020 and by National Funds through FCT—Fundao para a Ciłncia). SB acknowledges the Post-doc Grant from FCT, ref. SFRH/BPD/92342/2013. JF acknowledges the Doctoral Grant from FCT, ref. SFRH/BD/ 85376/2012. | Título da revista, periódico, livro ou evento: | Frontiers in Psychology | Volume: | 9 | Resumo: | We present an innovative and robust solution to both biometric and emotion identification using the electrocardiogram (ECG). The ECG represents the electrical signal that comes from the contraction of the heart muscles, indirectly representing the flow of blood inside the heart, it is known to convey a key that allows biometric identification. Moreover, due to its relationship with the nervous system, it also varies as a function of the emotional state. The use of information-theoretic data models, associated with data compression algorithms, allowed to effectively compare ECG records and infer the person identity, as well as emotional state at the time of data collection. The proposed method does not require ECG wave delineation or alignment, which reduces preprocessing error. The method is divided into three steps: (1) conversion of the real-valued ECG record into a symbolic time-series, using a quantization process; (2) conditional compression of the symbolic representation of the ECG, using the symbolic ECG records stored in the database as reference; (3) identification of the ECG record class, using a 1-NN (nearest neighbor) classifier. We obtained over 98% of accuracy in biometric identification, whereas in emotion recognition we attained over 90%. Therefore, the method adequately identify the person, and his/her emotion. Also, the proposed method is flexible and may be adapted to different problems, by the alteration of the templates for training the model. | URI: | https://hdl.handle.net/10316/107646 | ISSN: | 1664-1078 | DOI: | 10.3389/fpsyg.2018.00467 | Direitos: | openAccess |
Aparece nas coleções: | I&D IBILI - Artigos em Revistas Internacionais FMUC Medicina - Artigos em Revistas Internacionais |
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Ficheiro | Descrição | Tamanho | Formato | |
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Biometric and Emotion Identification An ECG Compression Based Method.pdf | 775.29 kB | Adobe PDF | Ver/Abrir |
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