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Effect of identity mapping, transfer learning and domain knowledge on the robustness and generalization ability of a network: a biometric based case study

  • Avantika SinghEmail author
  • Aditya Nigam
Original Research
  • 7 Downloads

Abstract

Abstract

This work evaluates two widely validated CNN models VGG and ResNet in the biometric domain based applications. These models have shown exceptional performance on the large ImageNet dataset but the predictive capability of these models for domain specific-tasks where limited data samples are present need to be checked. Here in this paper efforts have been made to analyze two interesting biometric problems (1) multi-class oculus classification (2) fingerprint sensor classification. The distinguish experimental results are evaluated on the benchmark datasets like IIIT-D, ND, IITK contact-lens datasets and FVC 2002, FVC 2004, FVC 2006, IIITD-MOLF and IITK fingerprint datasets. We have raised many interesting questions regarding the reliability and applicability of deep-learning models specifically for biometric based applications. Experimental results along with in-depth feature analysis have shown that indeed residual connections with pre-trained network provides good prior for model weights and thus helps in better generalization.

Graphical abstract

Keywords

Deep learning Transfer learning Contact lens detection Fingerprint sensor classification Robustness analysis 

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Indian Institute of Technology MandiMandiIndia

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