CaNViS: A cardiac and neurological-based verification system that uses wearable sensors

The prevalence of more portable physiological sensors in medical, lifestyle and security fields have ushered in more viable biometric attributes that can be used for the task of identification and authentication. The portability of these sensors also allows systems that require more than one signal source to be feasible and more practical. Once these biological signals are captured, they can then be combined for the purposes of authentication. The study proposes such a multi-factor biometric system, by fusing cardiac and neurological components captured with an electrocardiograph (ECG) and electroencephalograph (EEG) respectively and using them as a biometric attribute. Representing each of these components in a common format and fusing them at a feature level allows one to create a novel biometric system that is interoperable with different biological signal sources. The results indicate the system portrays a sufficient false rejection (FRR) and false acceptance rates (FAR). The results also show there is value in implementing multi-factor biological signal-based biometric systems using wearable sensors.

[1]  Arun Ross,et al.  Information fusion in biometrics , 2003, Pattern Recognit. Lett..

[2]  R. Palaniappan,et al.  Classification of biological signals using linear and nonlinear features , 2010, Physiological measurement.

[3]  Arun Ross,et al.  Multimodal biometrics: An overview , 2004, 2004 12th European Signal Processing Conference.

[4]  Hae-Jeong Park,et al.  Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method , 2002, IEEE Transactions on Biomedical Engineering.

[5]  Petre Stoica,et al.  Introduction to spectral analysis , 1997 .

[6]  Ana L. N. Fred,et al.  BITtalino: A Biosignal Acquisition System based on the Arduino , 2013, BIODEVICES.

[7]  Ana L. N. Fred,et al.  ECG Biometrics: Principles and Applications , 2013, BIOSIGNALS.

[8]  Mohammad Bagher Shamsollahi,et al.  Person Identification by Using AR Model for EEG Signals , 2007 .

[9]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[10]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[11]  Sharath Pankanti,et al.  BIOMETRIC IDENTIFICATION , 2000 .

[12]  M Poulos,et al.  Person Identification from the EEG using Nonlinear Signal Classification , 2002, Methods of Information in Medicine.

[13]  Ana L. N. Fred,et al.  Real Time Electrocardiogram Segmentation for Finger based ECG Biometrics , 2012, BIOSIGNALS.

[14]  Arun Ross,et al.  Information fusion in biometrics , 2003, Pattern Recognit. Lett..

[15]  Danilo P. Mandic,et al.  EEG Based Biometric Framework for Automatic Identity Verification , 2007, J. VLSI Signal Process..

[16]  F. Tenore,et al.  Low-cost electroencephalogram (EEG) based authentication , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[17]  R. Reilly,et al.  Combination of EEG and ECG for improved automatic neonatal seizure detection , 2007, Clinical Neurophysiology.

[18]  Pieter H. Hartel,et al.  The state of the art in abuse of biometrics , 2005 .

[19]  Venu Govindaraju,et al.  Evaluation of biometric spoofing in a multimodal system , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[20]  Sanjay Kumar Singh,et al.  Evaluation of Electrocardiogram for Biometric Authentication , 2012, J. Information Security.

[21]  M Hirshkowitz,et al.  Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. , 2001, Sleep medicine.

[22]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[23]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[24]  Lionel M. Ni,et al.  Smart Phone and Next Generation Mobile Computing , 2006 .

[25]  L. Benedicenti,et al.  The electroencephalogram as a biometric , 2001, Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555).

[26]  B. V. K. Vijaya Kumar,et al.  Subject identification from electroencephalogram (EEG) signals during imagined speech , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[27]  T. V. D. Hagen,et al.  Why Yule-Walker should not be used for autoregressive modelling , 1996 .

[28]  R. Ward,et al.  EMG and EOG artifacts in brain computer interface systems: A survey , 2007, Clinical Neurophysiology.

[29]  Richard A. Davis,et al.  Modified burg algorithms for multivariate subset autoregression , 2000 .

[30]  Dimitrios Hatzinakos,et al.  Heart Biometrics: Theory, Methods and Applications , 2011 .

[31]  Bin Hu,et al.  A pervasive EEG-based biometric system , 2011, UAAII '11.