Advertisement

Face Recognition Using Gabor Wavelet in MapReduce and Spark

  • Anh-Cang PhanEmail author
  • Hung-Phi Cao
  • Ho-Dat Tran
  • Thuong-Cang Phan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)

Abstract

Face recognition has become one of the important research areas and is used in wide range of applications. In addition to accuracy, traditional face recognition methods face challenges on time-consuming to identify and apply to distributed systems in a large data environment. To solve these problems, we proposed a facial recognition method using the Gabor wavelet technique and the MapReduce parallel processing model. We performed parallel processing at the extraction and recognition stage with the MapReduce model in the Spark environment. Experimental results show that the proposed method significantly improves the computing time and the accuracy of face recognition.

Keywords

Face recognition Gabor wavelet MapReduce Spark 

References

  1. 1.
    Bellakhdhar, F., Loukil, K., Abid, M.: Face recognition approach using Gabor Wavelets, PCA and SVM. Int. J. Comput. Sci. Issues (IJCSI), 10(2) (2013)Google Scholar
  2. 2.
    Gervei, O., Ayatollahi, A., Gervei, N.: 3D face recognition using modified PCA methods. World Acad. Sci. Eng. Technol. 39 (2010)Google Scholar
  3. 3.
    Jain, A.K., Klare, B., Park, U.: Face recognition: some challenges in forensics. In: 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011). IEEE (2011)Google Scholar
  4. 4.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1991. IEEE (1991)Google Scholar
  5. 5.
    Wang, C., et al.: Face recognition based on principle component analysis and support vector machine. In: 3rd International Workshop on Intelligent Systems and Applications (ISA). IEEE (2011)Google Scholar
  6. 6.
    Maillo, J., Triguero, I., Herrera, F.: A mapreduce-based k-nearest neighbor approach for big data classification. In: Trustcom/BigDataSE/ISPA, 2015, vol. 2. IEEE (2015)Google Scholar
  7. 7.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  8. 8.
    Liu, L.: Performance Comparison by Running Benchmarks on Hadoop, Spark, and HAMR. University of Delaware, Diss (2015)Google Scholar
  9. 9.
    Ranjani Priya, A.C., Sridhar, Dr, M.: Spark–an efficient framework for large scale data analytics. Int. J. Sci. Eng. Res. (2016)Google Scholar
  10. 10.
    Zaharia, M., et al.: Spark: cluster computing with working sets. HotCloud 10(10-10) (2010)Google Scholar
  11. 11.
    Bagwe, T., Darji, N., Gunjal, J., Vanjari, N.: Face Detection Using hadoop map-reduce framework. Int. J. Res. Advent Technol. (2015)Google Scholar
  12. 12.
    Karau, H., et al.: Learning spark: lightning-fast big data analysis. O’Reilly Media, Inc. (2015)Google Scholar
  13. 13.
    Divakar, M.A., Arakeri, M.P.: User authentication system using multimodal biometrics and MapReduce. In: Information and Communication Technology for Sustainable Development. Springer, Singapore, pp. 71–82 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anh-Cang Phan
    • 1
    Email author
  • Hung-Phi Cao
    • 1
  • Ho-Dat Tran
    • 1
  • Thuong-Cang Phan
    • 2
  1. 1.Vinh Long University of Technology EducationVinhlongVietnam
  2. 2.Can Tho UniversityCan ThoVietnam

Personalised recommendations