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Iris Image Compression Using Deep Convolutional Neural Networks.

Ehsaneddin Jalilian1, Heinz Hofbauer1, Andreas Uhl1

  • 1Department of Computer Science, University of Salzburg, Jakob-Haringer-Straße 2, 5020 Salzburg, Austria.

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Summary
This summary is machine-generated.

Deep learning models like DSSLIC offer superior iris data compression, preserving unique biometric traits for accurate recognition. This study validates their effectiveness against traditional methods.

Keywords:
deep learningiris compressioniris recognition

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Area of Science:

  • Biometrics and Pattern Recognition
  • Image Processing and Computer Vision
  • Data Compression

Background:

  • Iris recognition systems require efficient data compression due to large data volumes.
  • Deep neural networks show promise for image compression but raise concerns about biometric trait preservation.
  • Existing compression techniques may not adequately balance compression ratios with recognition accuracy.

Purpose of the Study:

  • To investigate the compression effectiveness of DSSLIC, a deep learning model for iris data.
  • To evaluate the impact of image compression on iris recognition system performance.
  • To compare DSSLIC against state-of-the-art lossy compression algorithms.

Main Methods:

  • Assessed DSSLIC and another deep learning compression technique for iris data.
  • Measured image quality using Full-Reference (MS-SSIM, LFBVS) and No-Reference (BRISQUE) metrics.
  • Compared DSSLIC performance against JPEG2000, JPEG, BPG, HEVC, VCC, and AV1 using recognition scores.

Main Results:

  • DSSLIC demonstrated superior compression performance compared to all tested traditional methods.
  • The deep learning approach showed promising recognition accuracy, indicating preservation of unique iris traits.
  • Image quality metrics correlated with recognition scores, providing insights into compression's impact.

Conclusions:

  • DSSLIC is a highly effective deep learning-based compression model for iris data.
  • Deep learning compression offers a better balance between compression efficiency and biometric recognition accuracy than traditional methods.
  • The findings support the use of DSSLIC for practical iris recognition systems requiring data compression.