HVS Based Face Recognition Using Slant Transform

  • Authors

    • K Veearaswamy
    • M Koteswara Rao
    • K Anithasheela
    • Ch Himabindu
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.34.19215
  • Slant Transform, HVS, Sub Blocks.

  • Abstract

    There are many person identification technologies like password, PIN, key, and token are used in many applications. Present, popular identification technology is face. In many applications the database is large. Hence, recognition with high speed is major challenge. This paper presents a recognition using HVS features in transform domain. Human visual system identifies perceptual important information in the images. Slant transform basis vector is sawtooth. It efficiently represents linear brightness variations along an image line. Hence, in this work HVS features in slant transform domain is explored for face recognition. Feature vector is based on HVS parameters. In this method image is decomposed into subblocks using Slant Transform. Important elements are identified using HVS weightage. Experiments are performed on bench mark face databases. Proposed method has better recognition performance than existing methods. Retrieval time is also less.

     

     

  • References

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  • How to Cite

    Veearaswamy, K., Koteswara Rao, M., Anithasheela, K., & Himabindu, C. (2018). HVS Based Face Recognition Using Slant Transform. International Journal of Engineering & Technology, 7(3.34), 313-315. https://doi.org/10.14419/ijet.v7i3.34.19215

    Received date: 2018-09-07

    Accepted date: 2018-09-07

    Published date: 2018-09-01