Advertisement

Towards Real Time Implementation of Sparse Representation Classifier (SRC) Based Heartbeat Biometric System

  • W. C. TanEmail author
  • H. M. Yeap
  • K. J. Chee
  • D. A. Ramli
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 307)

Abstract

Implementation of the heartbeat biometric system consists of four main stages which are heartbeat data acquisition, pre-processing and feature extraction, modeling and classification. In this study a new approach for classification method based on Sparse Representation Classifier (SRC) is proposed. By introducing kernel trick into SRC, the classification performance of the classifier can be further improved by implicitly map features data into a high-dimensional kernel feature space. Based on heart sound data, experimental results have shown a promising performance of KSRC with 85.45 % of accuracy has been achieved and a better performance has been observed by this classifier compared to Support Vector Machines (SVM), SRC and K-Nearest Neighbor (KNN). This achievement has proved the possibility of heartbeat as a biometric trait for human authentication system. Due to this, an extension in term of heartbeat data acquisition toward real time implementation is then proposed in this paper. Here, a wrist-mounted heartbeat sensor to sense the heartbeat signal is designed. This developed sensor is an electrometer which is capable to measure the properties of electrocardiogram (ECG) signal. The developed hardware has also shown its viability toward execution of heartbeat data acquisition in real time.

Keywords

Biometrics Heartbeat ECG Kernel trick Sparse representation classifier 

Notes

Acknowledgements

This work was supported by Universiti Sains Malaysia and Fundamental Research Grant Scheme (6071266).

References

  1. 1.
    Becker S, Bobin J, Candès EJ (2011) NESTA: a fast and accurate first-order method for sparse recovery. SIAM Journal on Imaging Sciences 4:1–39CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Beritelli F, Serrano S (2007) Biometric identification based on frequency analysis of cardiac sounds. Information Forensics and Security, IEEE Transactions on 2:596–604Google Scholar
  3. 3.
    Candès EJ, Romberg JK, Tao T (2006) Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics 59:1207–1223CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Candes EJ, Tao T (2006) Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? Information Theory, IEEE Transactions on 52:5406–5425CrossRefMathSciNetGoogle Scholar
  5. 5.
    Candès EJ, Wakin MB (2008) An introduction to compressive sampling. Signal Processing Magazine, IEEE 25:21–30Google Scholar
  6. 6.
    Donoho DL (2006) For most large underdetermined systems of linear equations the minimal Communications on Pure and Applied Mathematics 59:797–829CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    El-Bendary N, Al-Qaheri H, Zawbaa HM et al. (2010) HSAS: Heart Sound Authentication System. In: Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress on. p 351–356Google Scholar
  8. 8.
    Figueiredo MaT, Nowak RD, Wright SJ (2007) Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems. Selected Topics in Signal Processing, IEEE Journal of 1:586–597Google Scholar
  9. 9.
    Kim S, Eriksson T, Kang H-G et al. (2004) A pitch synchronous feature extraction method for speaker recognition. In: Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP’ 04). IEEE International Conference on. p I-405–408 vol. 401Google Scholar
  10. 10.
    Li Z, Wei-Da Z, Pei-Chann C et al. (2012) Kernel Sparse Representation-Based Classifier. Signal Processing. IEEE Transactions on 60:1684–1695Google Scholar
  11. 11.
    Memon S, Lech M, Ling H (2009) Using information theoretic vector quantization for inverted MFCC based speaker verification. In: Computer, Control and Communication, 2009. IC4 2009. 2nd International Conference on. p 1–5Google Scholar
  12. 12.
    Phua K, Dat TH, Chen J et al. (2006) Human identification using heart sound. In: Second International Workshop on Multimodal User Authentication, Toulouse, FranceGoogle Scholar
  13. 13.
    Spadaccini A, Beritelli F (2012) Performance Evaluation of Heart Sounds Biometric Systems on An Open Dataset. In: Proceedings of the 5th IAPR International Conference on BiometricsGoogle Scholar
  14. 14.
    Wright J, Yang AY, Ganesh A et al. (2009) Robust Face Recognition via Sparse Representation. Pattern Analysis and Machine Intelligence. IEEE Transactions on 31:210–227Google Scholar
  15. 15.
    Xiaoling Y, Baohua T, Jiehua D et al. (2010) Comparative Study on Voice Activity Detection Algorithm. In: Electrical and Control Engineering (ICECE), 2010 International Conference on. p 599–602Google Scholar
  16. 16.
    Yu K, Ji L, Zhang X (2002) Kernel nearest-neighbor algorithm. Neural Processing Letters 15:147–156CrossRefzbMATHGoogle Scholar
  17. 17.
    Zhao Z, Shen Q, Ren F (2013) Heart Sound Biometric System Based on Marginal Spectrum Analysis. Sensors 13:2530–2551CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • W. C. Tan
    • 1
    Email author
  • H. M. Yeap
    • 1
  • K. J. Chee
    • 1
  • D. A. Ramli
    • 1
  1. 1.Intelligent Biometric Research Group, School of Electrical and Electronic, Engineering CampusUniversiti Sains MalaysiaNibong TebalMalaysia

Personalised recommendations