귀 주변에서 측정한 유사 심전도 기반 개인 인증 시스템 개발 가능성
Feasibility of Using Similar Electrocardiography Measured around the Ears to Develop a Personal Authentication System
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A personal authentication system based on biosignals has received increasing attention due to its relatively high security as compared to traditional authentication systems based on a key and password. Electrocardiography (ECG) measured from the chest or wrist is one of the widely used biosignals to develop a personal authentication system. In this study, we investigated the feasibility of using similar ECG measured behind the ears to develop a personal authentication system. To this end, similar ECGs were measured from thirty subjects using a pair of three electrodes attached behind each of the ears during resting state during which the standard Lead-I ECG was also simultaneously measured from both wrists as baseline ECG. The three ECG components, Q, R, and S, were extracted for each subject as classification features, and authentication accuracy was estimated using support vector machine (SVM) based on a 5×5-fold cross-validation. The mean authentication accuracies of Lead I-ECG and similar ECG were 90.41 ± 8.26% and 81.15 ± 7.54%, respectively. Considering a chance level of 3.33% (=1/30), the mean authentication performance of similar ECG could demonstrate the feasibility of using similar ECG measured behind the ears on the development of a personal authentication system.
Abstract
A personal authentication system based on biosignals has received increasing attention due to its relatively high security as compared to traditional authentication systems based on a key and password. Electrocardiography (ECG) measured from the chest or wrist is one of the widely used biosignals to develop a personal authentication system. In this study, we investigated the feasibility of using similar ECG measured behind the ears to develop a personal authentication system. To this end, similar ECGs were measured from thirty subjects using a pair of three electrodes attached behind each of the ears during resting state during which the standard Lead-I ECG was also simultaneously measured from both wrists as baseline ECG. The three ECG components, Q, R, and S, were extracted for each subject as classification features, and authentication accuracy was estimated using support vector machine (SVM) based on a 5×5-fold cross-validation. The mean authentication accuracies of Lead I-ECG and similar ECG were 90.41 ± 8.26% and 81.15 ± 7.54%, respectively. Considering a chance level of 3.33% (=1/30), the mean authentication performance of similar ECG could demonstrate the feasibility of using similar ECG measured behind the ears on the development of a personal authentication system.
참고문헌 (33)
- Jain AK, "An introduction to biometric recognition", IEEE Trans Circuits Syst Video Technol, 14, pp.4~20 (2014)
- Isenor DK, "Fingerprint identification using graph matching", Pattern Recognit, 19(2), pp.113~122 (1986)
- Hrechak AK, "Automated fingerprint recognition using structural matching", Pattern Recognit, 23(8), pp.893~904 (1990)
- Ma L, "Personal identification based on iris texture analysis", IEEE Trans Pattern Anal Mach Intell, 25(12), pp.1519~1533 (2003)
- Ahonen T, "Face description with local binary patterns : application to face recognition", IEEE Trans Pattern Anal Mach Intell, 12, pp.2037~2041 (2016)
- Rajagopal G, "Personal authentication using multifeatures multispectral palm print traits", Sci, 2015, pp.1~12 (2015)
- Klonowski M, "User authorization based on hand geometry without special equipment", Pattern Recognit, 73, pp.189~201 (2018)
- Sun, Y, "Deep learning face representation by joint identification-verification", Adv Neural Inf Process Syst, pp.1988~1996 (2014)
- Haware S, "Retina based biometric identification using SURF and ORB feature descriptors", Conf Proc In 2017International conference on Microelectronic Devices, Circuits and Systems, pp.1~6 (2017)
- Shaheed K, "A systematic review of finger vein recognition techniques", Information, 9(9), pp.213~ (2018)
- Chen CH, "High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier", Expert Syst Appl, 36(7), pp.10351~10356 (2009)
- Rai H, "Iris recognition using combined support vector machine and Hamming distance approach", Expert Syst Appl, 41(2), pp.588~593 (2014)
- Biel L, "ECG analysis : a new approach in human identification", IEEE Trans Instrum Meas, 50(3), pp.808~812 (2001)
- Shen TWD, "Implementation of a onelead ECG human identification system on a normal population", J Eng Comput Innov, 2(1), pp.12~21 (2010)
- Kang SJ, "ECG authentication system design based on signal analysis in mobile and wearable devices", IEEE Signal Process Lett, 23(6), pp.805~808 (2016)
- Hacking mobile phones using 2D printed fingerprints, "Cao K", Michigan State University, 1~3, pp.Michigan State University Tech Report MSUCSE-16-2 (2016)
- Erdogmus N, "Spoofing face recognition with 3D masks", IEEE Trans Inf Forensic Secur, 9(7), pp.1084~1097 (2014)
- Gradl S, "Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices", Conf Proc In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.2452~2455 (2012)
- Crawford J, "Ten steps to recording a standard 12-lead ECG", Practice Nursing, 21(12), pp.622~630 (2010)
- Zhang Q, "A machine learning-empowered system for long-term motion-tolerant wearable monitoring of blood pressure and heart rate with Ear-ECG", PPG, 2017, pp.5, 10547~10561
- Da He D, "An ear-worn vital signs monitor", IEEE Trans Biomed Eng, 62(11), pp.2547~2552 (2015)
- Wübbeler G, "Verification of humans using the electrocardiogram", Pattern Recognit Lett, 28(10), pp.1172~1175 (2007)
- Israel SA, "ECG to identify individuals", Pattern Recognit, 38(1), pp.133~142 (2005)
- Chan AD, "Wavelet distance measure for person identification using electrocardiograms", IEEE Trans Instrum Meas, 57(2), pp.248~253 (2008)
- Osselton JW, "Acquisition of EEG data by bipolar unipolar and average reference methods: a theoretical comparison", Electroencephalogr. Clin Neurophysiol, 19, pp.527~528 (1965)
- Wang Y, "Analysis of human electrocardiogram for biometric recognition", Conf Proc EURASIP journal on Advances in Signal Processing, 1, pp.148658~ (2008)
- Shen TW, "One-lead ECG for identity verification", Conf Proc In the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, 1, pp.62~63 (2002)
- Hassan Z, "Review of fiducial and nonfiducial techniques of feature extraction in ECG based biometric systems", Indian J Sci Technol, 9(21), pp.23~26 (2016)
- Sun Y, "Characteristic wave detection in ECG signal using morphological transform", BMC Cardiovasc Disord, 5(1), pp.28~ (2005)
- O’Haver T, "Command-line findpeaks MATLAB function", ~
- Übeyli ED, "ECG beats classification using multiclass support vector machines with error correcting output codes", Digit Signal Prog, 17(3), pp.675~684 (2007)
- Acır N, "A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems", Expert Syst Appl, 31(1), pp.150~158 (2006)
- Saini I, "Electrocardiogram beat classification using empirical mode decomposition and multiclass directed acyclic graph support vector machine", Comput Electr Eng, 40(5), pp.1774~1787 (2014)