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Face Recognition Using Invariance with a Single Training Sample.

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  • معلومة اضافية
    • Author-Supplied Keywords:
      face recognition
      invariance model
      single training sample
    • NAICS/Industry Codes:
      541910 Marketing Research and Public Opinion Polling
    • Abstract:
      For the limits of memories and computing performance of current intelligent terminals, it is necessary to develop some strategies which can keep the balance of the accuracy and running time for face recognition. The purpose of the work in this paper is to find the invariant features of facial images and represent each subject with only one training sample for face recognition. We propose a two-layer hierarchical model, called invariance model, and its corresponding algorithms to keep the balance of accuracy, storage and running time. Especially, we take advantages of wavelet transformations and invariant moments to obtain the key features as well as reduce dimensions of feature data based on the cognitive rules of human brains. Furthermore, we improve usual pooling methods, e.g. max pooling and average pooling, and propose the weighted pooling method to reduce dimensions with no effect on accuracy, which let storage requirement and recognition time greatly decrease. The simulation results show that the proposed method does better than some typical and nearly-proposed algorithms in balancing the accuracy and running time. [ABSTRACT FROM AUTHOR]
    • Abstract:
      Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
    • Author Affiliations:
      1National ASIC Engineering Research Center, Southeast University, Nanjing, China
    • ISSN:
      1693-6930
    • Accession Number:
      10.12928/TELKOMNIKA.v12i4.810
    • Accession Number:
      111112474
  • Citations
    • ABNT:
      QIAN TIAN. Face Recognition Using Invariance with a Single Training Sample. Telkomnika, [s. l.], v. 12, n. 4, p. 921–932, 2014. DOI 10.12928/TELKOMNIKA.v12i4.810. Disponível em: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=111112474&custid=s8280428. Acesso em: 23 fev. 2020.
    • AMA:
      Qian Tian. Face Recognition Using Invariance with a Single Training Sample. Telkomnika. 2014;12(4):921-932. doi:10.12928/TELKOMNIKA.v12i4.810.
    • APA:
      Qian Tian. (2014). Face Recognition Using Invariance with a Single Training Sample. Telkomnika, 12(4), 921–932. https://doi.org/10.12928/TELKOMNIKA.v12i4.810
    • Chicago/Turabian: Author-Date:
      Qian Tian. 2014. “Face Recognition Using Invariance with a Single Training Sample.” Telkomnika 12 (4): 921–32. doi:10.12928/TELKOMNIKA.v12i4.810.
    • Harvard:
      Qian Tian (2014) ‘Face Recognition Using Invariance with a Single Training Sample’, Telkomnika, 12(4), pp. 921–932. doi: 10.12928/TELKOMNIKA.v12i4.810.
    • Harvard: Australian:
      Qian Tian 2014, ‘Face Recognition Using Invariance with a Single Training Sample’, Telkomnika, vol. 12, no. 4, pp. 921–932, viewed 23 February 2020, .
    • MLA:
      Qian Tian. “Face Recognition Using Invariance with a Single Training Sample.” Telkomnika, vol. 12, no. 4, Dec. 2014, pp. 921–932. EBSCOhost, doi:10.12928/TELKOMNIKA.v12i4.810.
    • Chicago/Turabian: Humanities:
      Qian Tian. “Face Recognition Using Invariance with a Single Training Sample.” Telkomnika 12, no. 4 (December 2014): 921–32. doi:10.12928/TELKOMNIKA.v12i4.810.
    • Vancouver/ICMJE:
      Qian Tian. Face Recognition Using Invariance with a Single Training Sample. Telkomnika [Internet]. 2014 Dec [cited 2020 Feb 23];12(4):921–32. Available from: http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=111112474&custid=s8280428