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Related Concept Videos

Veins of Upper Limbs01:17

Veins of Upper Limbs

The human circulatory system, a marvel of biological engineering, is a complex network of vessels that transport blood throughout the body. Among these, the veins responsible for carrying blood from the upper limbs are divided into two categories: deep and superficial.
The deep venous system is primarily composed of the ulnar and radial veins. The ulnar vein, which drains the fingers through the superficial palmar venous arches, and the radial vein, which serves the palms via the deep palmar...
Veins of Lower Limbs01:15

Veins of Lower Limbs

The human body consists of an intricate network of veins responsible for the crucial task of blood drainage from the lower limbs. These veins can be categorized into two main types: deep veins and superficial veins.
Formed by the union of the medial and lateral plantar veins, the posterior tibial vein, rising through the calf muscle, assimilates the fibular vein. The anterior tibial vein, a superior extension of the foot's dorsalis pedis vein, merges with the posterior tibial vein at the knee,...

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Related Experiment Video

Updated: May 8, 2026

A New Best Practice for Validating Tail Vein Injections in Rat with Near-infrared-Labeled Agents
04:19

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Published on: April 19, 2019

Finger vein recognition based on personalized weight maps.

Gongping Yang1, Rongyang Xiao, Yilong Yin

  • 1School of Computer Science and Technology, Shandong University, Jinan 250101, China. ylyin@sdu.edu.cn.

Sensors (Basel, Switzerland)
|September 13, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces Personalized Weight Maps (PWMs) for finger vein recognition. PWMs improve accuracy by assigning unique importance to different feature bits, enhancing biometric security.

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Area of Science:

  • Biometrics
  • Pattern Recognition
  • Computer Vision

Background:

  • Finger vein recognition is a biometric technology using finger vein patterns for identity verification.
  • Existing binary pattern methods for finger vein recognition face challenges in feature extraction and matching.
  • Current methods assign equal importance to all feature bits, limiting accuracy.

Purpose of the Study:

  • To propose a novel finger vein recognition method using Personalized Weight Maps (PWMs).
  • To enhance the accuracy and reliability of binary pattern-based biometric systems.
  • To address the limitation of equal feature bit weighting in existing methods.

Main Methods:

  • Developed a finger vein recognition framework incorporating preprocessing, feature extraction, and matching.
  • Introduced the concept of Personalized Weight Maps (PWMs) where feature bits are weighted based on individual sample stability.
  • Implemented and evaluated the PWM-based approach through extensive experiments.

Main Results:

  • The proposed PWM method demonstrated superior performance compared to existing techniques.
  • Experimental results confirmed high robustness and reliability of the PWM approach.
  • PWM achieved significant improvements in finger vein recognition accuracy.

Conclusions:

  • Personalized Weight Maps (PWMs) offer a more effective approach to finger vein recognition.
  • The PWM framework provides a generalizable solution for binary pattern-based recognition systems.
  • This method enhances the overall security and reliability of biometric authentication.