Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

@article{Bhilare2018SinglesensorHM,
  title={Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach},
  author={Shruti Bhilare and Gaurav Jaswal and Vivek Kanhangad and Aditya Nigam},
  journal={Machine Vision and Applications},
  year={2018},
  volume={29},
  pages={1269-1286}
}
Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user… CONTINUE READING

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Key Quantitative Results

  • The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively.
  • On the CASIA dataset, palm-vein achieves an accuracy of 98%, while the best performance among the finger-veins was achieved by the middle finger with a recognition rate of 94.50%.
  • In particular, the proposed approach achieves equal error rates of 0.13 and 1.21%, and rank-1 identification rates of 100 and 100% on the in-house and CASIA datasets, respectively.

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References

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