Earticle

초록

영어

It has been found that poor quality images decrease the performance of finger vein recognition system, due to missing, vague or spurious features. Therefore, it is important for a finger vein recognition system to evaluate the quality of finger vein images. In this paper, a new method based on Support Vector Regression (SVR) is proposed for finger vein image quality evaluation. In our method, we first manually annotate quality scores for finger vein images in training set and extract five quality features of these images. Then quality scores and quality features are used to build a SVR model, which will be applied to evaluate quality for testing images. In addition, we explore the use of quality score and ascertain that quality score can be used as ancillary information to enhance recognition accuracy for finger vein. Experimental results show that our proposed method is effective for finger vein image quality evaluation.

목차

Abstract
 1. Introduction
 2. The Proposed Method
  2.1 Quality Scores Annotation
  2.2 Quality Evaluation Features Selection
  2.3 Learning SVR Model and Prediction
 3. Experimental Results and Analysis
  3.1 The Experimental Database
  3.2 Experiments Setting
  3.3 Experiment 1
  3.4 Experiment 2
  3.5 Experiment 3
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Lizhen Zhou [ School of Computer Science and Technology, Shandong University, Jinan, 250101, P.R. China ]
• Gongping Yang [ School of Computer Science and Technology, Shandong University, Jinan, 250101, P.R. China ]
• Lu Yang [ School of Computer Science and Technology, Shandong University, Jinan, 250101, P.R. China ]
• Yilong Yin [ School of Computer Science and Technology, Shandong University, Jinan, 250101, P.R. China ]
• Ying Li [ Shandong Management University, Jinan, 250357, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605