Abstract
ECG has drawn increasing attention in the biometrics and achieves great success compared with other biological characteristics. However, ECG cannot satisfy the requirements of mobile application owing to the poor quality. In this paper, we learn discriminative representation for ECG biometrics based on multi-scale 1D-PDV feature. First, we choose PDV as the base feature and attempt to convert PDV to the one-dimensional and multi-scale in the ECG biometrics. Second, our method learns a mapping to project the multi-scale 1D-PDV to a low dimensional feature vector and capture discriminative information of ECG. Then each feature vector is pooled in the codebook and represented as a histogram feature. Last, we apply principal component analysis (PCA) to reduce the histogram feature dimension and compute the matching score with cosine similarity. We evaluate our method on two public databases and the results prove our method achieves superior performance than other existing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Biel, L., Pettersson, O., Philipson, L., Wide, P.: ECG analysis: a new approach in human identification. IEEE Trans. Instrum. Meas. 50, 808–812 (2001)
Jain, A.K., Nandakumar, K., Ross, A.: 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn. Lett. 79, 80–105 (2016)
Kun, S., et al.: Human identification using finger vein and ECG signals. Neurocomputing 332, 111–118 (2019)
Louis, W., Komeili, M., Hatzinakos, D.: Continuous authentication using one-dimensional multi-resolution local binary patterns (1DMRLBP) in ECG biometrics. IEEE Trans. Inf. Forensics Secur. 11(12), 2818–2832 (2016)
Liu, H.Y., Yang, L., Yang, G.P., et al.: Discriminative binary: descriptor for finger vein recognition. IEEE Access 6, 5795–5804 (2018)
Lu, J.W., Liong, V.E., Zhou, X.Z., et al.: Learning compact binary face descriptor for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37, 2041–2056 (2015)
Lu, J.W., Liong, V.E., Zhou, J.: Simultaneous local binary feature learning and encoding for homogeneous and heterogeneous face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1979–1993 (2018)
Liu, H., Yang, G., Yang, L., Su, K., Yin, Y.: Anchor-based manifold binary pattern for finger vein recognition. SCIENCE CHINA Inf. Sci. 62(5), 52104:1–52104:16 (2019)
Liu, F., Yin, Y.L., Yang, G.P., et al.: Finger vein recognition with super-pixel based features. In: Proceedings of IEEE International Joint Conference on Biometrics (2014)
Agrafioti, F., Hatzinakos, D.: ECG based recognition using second order statistics. In: 6th Annual IEEE Communication Networks and Services Research Conference, CNSR 2008 (2008)
Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 3, 230–236 (1985)
Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for scalable image retrieval. In: Proceedings of the IEEE Computer Society Conference on omputer Vision and Pattern Recognition, pp. 3424–3431 (2010)
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)
Wen, Z., Yin, W.: A feasible method for optimization with orthogonality constraints. Math. Program. 142(1), 397–434 (2013)
Dong, L., Yang, G., Yin, Y., Liu, F., Xi, X.: Finger vein verification based on a personalized best patches map. In: Proceedings IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8, October 2014
Berkaya, S.K., Uysal, A.K., Gunal, E.S., Ergin, S., Günal, S., Bilginer Gülmezoglu, M.: A survey on ECG analysis. Biomed. Signal Process. Control 43, 216–235 (2018)
Zhao, Z.D., Chen, D.D., Luo, Y.: A human ECG identification system based on ensemble empirical mode decomposition. Sensors 13(5) (2013). https://doi.org/10.3390/s130506832
Lu, W., Hou, H.H., Chu, J.H.: Feature fusion for imbalanced ECG data analysis. Biomed. Signal Process. Control. 41, 52–160 (2018)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61703235,61876098 and in part by the Key Research and Development Project of Shandong Province under Grant 2018GGX101032 and 2019GGX101056.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sun, Y., Yang, G., Huang, Y., Wang, K., Yin, Y. (2019). Learning Discriminative Representation for ECG Biometrics Based on Multi-Scale 1D-PDV. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-030-31456-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31455-2
Online ISBN: 978-3-030-31456-9
eBook Packages: Computer ScienceComputer Science (R0)