An efficient finger vein indexing scheme based on unsupervised clustering

@article{Raghavendra2015AnEF,
  title={An efficient finger vein indexing scheme based on unsupervised clustering},
  author={Ramachandra Raghavendra and Jayachander Surbiryala and Christoph Busch},
  journal={IEEE International Conference on Identity, Security and Behavior Analysis (ISBA 2015)},
  year={2015},
  pages={1-8}
}
Finger vein recognition has emerged as the robust biometric modality because of their unique vein pattern that can be captured using near infrared spectrum. The large scale finger vein based biometric solutions demand the need of searching the probe finger vein sample against the large collection of gallery samples. In order to improve the reliability in searching for the suitable identity in the large-scale finger vein database, it is essential to introduce the finger vein indexing and… CONTINUE READING

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Key Quantitative Results

  • Further, the multi-cluster search demonstrated the performance with pre-selection error rate of 0.98% (hit rate of 99.02%) with a penetration rate of 52.88%.

Citations

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Review of personal identification based on near infrared vein imaging of finger

2017 2nd International Conference on Image, Vision and Computing (ICIVC) • 2017

Binary search path of vocabulary tree based finger vein image retrieval

2016 International Conference on Biometrics (ICB) • 2016
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Finger vein indexing based on binary features

2015 Colour and Visual Computing Symposium (CVCS) • 2015
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Presentation Attack Detection Algorithms for Finger Vein Biometrics: A Comprehensive Study

2015 11th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) • 2015
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