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Related Experiment Video

Updated: Apr 6, 2026

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Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex.

Shan Juan Xie1, Yu Lu2, Sook Yoon3

  • 1Institute of Remote Sensing and Earth Science, College of Science, Hangzhou Normal University, Hangzhou 311121, China. shanj_x@hotmail.com.

Sensors (Basel, Switzerland)
|July 18, 2015
PubMed
Summary

Finger vein recognition systems (FVRSs) struggle with poor image quality due to varying finger tissues. A new intensity variation normalization method using guided filter based single scale retinex (GFSSR) effectively enhances image quality and recognition accuracy.

Keywords:
finger vein recognitionguided filterimage enhancementintensity variationsingle scale retinex

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Area of Science:

  • Biometrics and Authentication
  • Image Processing
  • Medical Imaging

Background:

  • Finger vein recognition is a promising biometric for personal authentication.
  • Variations in finger tissue composition (bone, muscle, water, fat) lead to poor image quality in finger vein recognition systems (FVRSs).
  • Degraded image quality significantly impacts the performance of FVRSs.

Purpose of the Study:

  • To analyze intrinsic factors of finger tissue composition that cause poor finger vein image quality.
  • To propose an effective image enhancement method for FVRSs.
  • To improve the accuracy of finger vein recognition.

Main Methods:

  • Analysis of intrinsic factors contributing to poor finger vein image quality.
  • Development of an intensity variation (IV) normalization method.
  • Application of guided filter based single scale retinex (GFSSR) for image enhancement.

Main Results:

  • The proposed GFSSR method effectively addresses intensity variations in finger vein images.
  • Experimental results show significant enhancement in finger vein image quality.
  • The method demonstrated improved finger vein recognition accuracy on public datasets.

Conclusions:

  • The proposed GFSSR-based IV normalization method is effective for enhancing finger vein images.
  • Improved image quality leads to higher accuracy in finger vein recognition systems.
  • This method offers a viable solution for overcoming quality issues in biometric authentication.