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  • Iris and Periocular Recognition using Deep Learning

    Iris and Periocular Recognition using Deep Learning by Kumar, Ajay;

      • GET 10% OFF

      • The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
      • Publisher's listprice EUR 140.99
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        59 807 Ft (56 959 Ft + 5% VAT)
      • Discount 10% (cc. 5 981 Ft off)
      • Discounted price 53 826 Ft (51 263 Ft + 5% VAT)

    59 807 Ft

    db

    Availability

    printed on demand

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Long description:

    Iris and Periocular Recognition using Deep Learning systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many applications in sectors such as healthcare, online education, e-business, metaverse, and entertainment. This is the first-ever book devoted to iris recognition that details cutting-edge techniques using deep neural networks. This book systematically introduces such algorithmic details with attractive illustrations, examples, experimental comparisons, and security analysis. It answers many fundamental questions about the most effective iris and periocular recognition techniques.

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    Table of Contents:

    1. Advances in Iris and Ocular Recognition: An Insight into Trends
    2. Unlocking the Full Potential of Iris Recognition with Deep Learning
    3. Real-Time Online Framework for Accurate Detection, Segmentation, and Recognition of Irises
    4. Enhancing Iris Recognition Accuracy through Dilated Residual Features
    5. Iris Recognition with Deep Learning Across Spectrums
    6. Semantics-Assisted Convolutional Neural Network for Accurate Periocular Recognition
    7. Deep Neural Network with Focused Attention on Critical Periocular Regions
    8. Dynamic Iris Recognition through Multi-Feature Collaboration
    9. Position-Specific Convolutional Neural Network to Accurately Match Iris and Periocular Images
    10. Securing the Metaverse with Egocentric Iris Recognition via AR/VR/MR Devices
    11. Inference and Future Pathways: Reflections and Exploration of New Horizons

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