skip to main content
10.1145/3348488.3348492acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaivrConference Proceedingsconference-collections
research-article

Face Recognition System Based on Modified Sparse Representation

Published: 27 July 2019 Publication History

Abstract

This paper proposes a face recognition method based on modified sparse representation. Sparse representation is an advanced data analysis algorithm based on compressive sensing. Traditionally, the sparse representation is performed on the global dictionary formed by all the training classes. Afterwards, the classification is made based on the reconstruction errors. This method did not consider the individual representation capabilities of different classes. So, a modified sparse representation is designed in this study by conducting the sparse representation on the local dictionary formed by each training class. Then, the reconstruction error of each class is computed and compared to determine the label of the test sample. In the experiments, the AR and Yale-B face image databases are employed to investigate the performance of the proposed method. The results show its effectiveness and robustness.

References

[1]
Turk, M. A. and Pentland, A. P. 1991. Face recognition using eigen-faces. Proc. Of IEEE Conf. Computer Vision and Pattern Recognition, 1991, 586--591.
[2]
He, X., Yan, S. and Hu, Y., et al. 2005. Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell., 27, 3, 328--340.
[3]
Wang, Y. and Wu, Y. 2010. Face recognition using Intrinsicfaces. Pattern Recognition, 43, 3580--3590.
[4]
Wen, Y., He, L., and Shi, P. 2012. Face recognition using difference vector plus KPCA. Digital Signal Processing, 22, 1, 140--146.
[5]
Choi, J. Y., Ro, Y. M., and Plataniotis, K. N. 2012. Color local texture features for color face recognition. IEEE Transactions on Image Processing, 21, 3, 1366--1380.
[6]
Lu, J., Liong, V. E., and Zhuang, X., et al. 2015. Learning compact binary face descriptor for face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 37, 10, 2041--2056.
[7]
Zhang, Y., Liu, C. 2003. Face recognition based on support vector machine and nearest neighbor classifier. Journal of Systems Engineering and Electronics, 14, 3, 73--76.
[8]
Wright, J., Yang, A., and Ganesh, A., et al. 2009. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell., 31, 5, 210--227.
[9]
Lu, J., Wang, G., and Deng, W., et al. 2017. Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans Image Process, 26, 8, 4042--4054.
[10]
Wagner, A., Wright, J., and Ganesh, et al. 2012. Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell., 34, 2, 372--386.
[11]
Ran, H., Zheng, W. S., and Hu, B. G., et al. 2012. Two-stage nonnegative sparse representation for large-scale face recognition. IEEE Transactions on Neural Networks & Learning Systems, 24, 1, 35--46.
[12]
Ding, C. and Tao, D. 2015. Robust face recognition via multimodal deep face representation [J]. IEEE Trans. Multimedia, 17, 11, 2049--2058.
[13]
Gao, S., Zhang, Y., and Jia, K., et al. 2017. Single sample face recognition via learning deep supervised autoencoders. IEEE Transactions on Information Forensics & Security, 10, 10, 2108--2118.
[14]
Wimalajeewa, T., Varshney, P. K. 2014. OMP based joint sparsity pattern recovery under communication constraints. IEEE Trans. Signal Process., 62, 19, 5059--5072.

Index Terms

  1. Face Recognition System Based on Modified Sparse Representation

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    AIVR 2019: Proceedings of the 2019 3rd International Conference on Artificial Intelligence and Virtual Reality
    July 2019
    80 pages
    ISBN:9781450371612
    DOI:10.1145/3348488
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Nanyang Technological University
    • University of Tsukuba: University of Tsukuba

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 July 2019

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. face recognition
    2. local dictionary
    3. sparse representation

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    AIVR 2019

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 66
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 27 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media