An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion

Jian XIAO; Sizhuo LI; Wei DONG; Qinghua LI; Fang HU

doi:10.11999/JEIT200904

Volume 43 Issue 10

Oct. 2021

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2021 > 43(10): 3010-3017

Jian XIAO, Sizhuo LI, Wei DONG, Qinghua LI, Fang HU. An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3010-3017. doi: 10.11999/JEIT200904

Citation:

Jian XIAO, Sizhuo LI, Wei DONG, Qinghua LI, Fang HU. An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion[J]. Journal of Electronics & Information Technology, 2021, 43(10): 3010-3017. doi: 10.11999/JEIT200904

Citation:

PDF( 2433 KB)

An Identity Recognition Method Based on ElectroCardioGraph and PhotoPlethysmoGraph Feature Fusion

doi: 10.11999/JEIT200904

School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, China

Funds: The Key Project of Research and Development Program of Shaanxi Province of China (2021GY-54),Xi’an Science and Technology Innovation Guiding Project (20180504YD23CG29(1))

Received Date: 2020-10-22
Rev Recd Date: 2021-03-16

Available Online: 2021-04-12

Publish Date: 2021-10-18

Abstract

Abstract

Because single mode ElectroCardioGraph (ECG) and PhotoPlethysmoGraph(PPG) existed problem with the low recognition accuracy, not considering intra-class correlation, this paper proposes a recognition method based on the Discriminant Correlation Analysis (DCA) for the feature layer fusion of the ECG and PPG combined feature matrix and the fusion of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers at the decision layer. The experimental results show that the use of fusion features (ECG-PPG) and fusion the classifier (KNN-SVM) method can classify and recognize 23 subjects with an accuracy of 98.2%, and the recognition accuracy is better than single-modal recognition in the conventional environment. It provides an effective model for multimodal biometric identification.
- ElectroCardioGraph (ECG),
- PhotoPlethysmoGraph (PPG),
- Multimodal biometric recognition,
- Feature fusion,
- Discriminant Correlation Analysis (DCA)

FullText(HTML)

References(20)

References

[1]	KAUR G, SINGH G, and KUMAR V. A review on biometric recognition[J]. International Journal of Bio-Science and Bio-Technology, 2014, 6(4): 69–76. doi: 10.14257/ijbsbt.2014.6.4.07
[2]	ROSS A A, NANDAKUMAR K J, and JAIN A K. Handbook of Multibiometrics[M]. New York: Springer, 2006: 37–58.
[3]	ROSS A and JAIN A. Information fusion in biometrics[J]. Pattern Recognition Letters, 2003, 24(13): 2115–2125. doi: 10.1016/S0167-8655(03)00079-5
[4]	张敏贵, 潘泉, 张洪才, 等. 多生物特征识别[J]. 信息与控制, 2002, 31(6): 524–528. doi: 10.3969/j.issn.1002-0411.2002.06.010 ZHANG Mingui, PAN Quan, ZHANG Hongcai, et al. Multibiometrics identification techniques[J]. Information and Control, 2002, 31(6): 524–528. doi: 10.3969/j.issn.1002-0411.2002.06.010
[5]	CHAA M, BOUKEZZOULA N E, and MERAOUMIA A. Features-level fusion of reflectance and illumination images in finger-knuckle-print identification system[J]. International Journal on Artificial Intelligence Tools, 2018, 27(3): 1850007. doi: 10.1142/S0218213018500070
[6]	CHEN Junkai, CHEN Zenghai, CHI Zheru, et al. Facial expression recognition in video with multiple feature fusion[J]. IEEE Transactions on Affective Computing, 2018, 9(1): 38–50. doi: 10.1109/TAFFC.2016.2593719
[7]	GUPTA P. Multibiometric authentication system using slap fingerprints, palm dorsal vein, and hand geometry[J]. IEEE Transactions on Industrial Electronics, 2018, 65(12): 9777–9784. doi: 10.1109/TIE.2018.2823686
[8]	HAMMAD M and WANG Kuanquan. Parallel score fusion of ECG and fingerprint for human authentication based on convolution neural network[J]. Computers & Security, 2019, 81: 107–122. doi: 10.1016/j.cose.2018.11.003
[9]	ARTEAGA-FALCONI J S, AL OSMAN H, and EL SADDIK A. ECG and fingerprint bimodal authentication[J]. Sustainable Cities and Society, 2018, 40: 274–283. doi: 10.1016/j.scs.2017.12.023
[10]	BASHAR K. ECG and EEG based multimodal biometrics for human identification[C]. 2018 IEEE International Conference on Systems, Man, and Cybernetics, Miyazaki, Japan, 2018: 4345–4350.
[11]	杨宜蒙. 基于ECG和PPG信号身份识别算法的研究[D]. [硕士论文], 哈尔滨工业大学, 2016. YANG Yimeng. A study of ECG & PPG-based biometrics technology[D]. [Master dissertation], Harbin Institute of Technology, 2016.
[12]	SUN Quansen, ZENG Shenggen, LIU Yan, et al. A new method of feature fusion and its application in image recognition[J]. Pattern Recognition, 2005, 38(12): 2437–2448. doi: 10.1016/j.patcog.2004.12.013
[13]	CORREA N M, ADALI T, LI Yiou, et al. Canonical correlation analysis for data fusion and group inferences[J]. IEEE Signal Processing Magazine, 2010, 27(4): 39–50. doi: 10.1109/MSP.2010.936725
[14]	HAGHIGHAT M, ABDEL-MOTTALEB M, and ALHALABI W. Fully automatic face normalization and single sample face recognition in unconstrained environments[J]. Expert Systems with Applications, 2016, 47: 23–34. doi: 10.1016/j.eswa.2015.10.047
[15]	GAO Xizhan, SUN Quansen, and YANG Jing. MRCCA: A novel CCA based method and its application in feature extraction and fusion for matrix data[J]. Applied Soft Computing, 2018, 62: 45–56. doi: 10.1016/j.asoc.2017.10.008
[16]	胡敏, 滕文娣, 王晓华, 等. 融合局部纹理和形状特征的人脸表情识别[J]. 电子与信息学报, 2018, 40(6): 1338–1344. doi: 10.11999/JEIT170799 HU Min, TENG Wendi, WANG Xiaohua, et al. Facial expression recognition based on local texture and shape features[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1338–1344. doi: 10.11999/JEIT170799
[17]	GUO Manshan, YANG Xu, ZHANG Feng, et al. Supervised dictionary learning supported classifier with feature fusion scheme to noninvasively detect TRISO-particle defects[J]. Journal of Nuclear Materials, 2019, 523: 43–50. doi: 10.1016/j.jnucmat.2019.05.040
[18]	SCHOTT J R. Principles of multivariate analysis: A user’s perspective[J]. Journal of the American Statistical Association, 2002, 97(458): 657–658. doi: 10.1198/jasa.2002.s479
[19]	HAGHIGHAT M, ABDEL-MOTTALEB M, and ALHALABI W. Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(9): 1984–1996. doi: 10.1109/TIFS.2016.2569061
[20]	TURK M and PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71–86. doi: 10.1162/jocn.1991.3.1.71