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CCRNet: a novel data-driven approach to improve cross-domain Iris recognition

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Abstract

In spite of the prominence and robustness of iris recognition systems, iris images acquisition using heterogeneous cameras/sensors, is the prime concern in deploying them for wide-scale applications. The textural qualities of iris samples (images) captured through distinct sensors substantially differ due to the differences in illumination and the underlying hardware that yields intra-class variation within the iris dataset. This paper examines three miscellaneous configurations of convolution and residual blocks to improve cross-domain iris recognition. Further, the finest architecture amongst three is identified by the Friedman test, where the statistical differences in proposed architectures are identified based on the outcomes of Nemeny and Bonferroni-Dunn tests. The quantitative performances of these architectures are perceived on several experiments simulated on two iris datasets; ND-CrossSensor-Iris-2013 and ND-iris-0405. The finest model is referred to as “Collaborative Convolutional Residual Network (CCRNet)” and is further examined on several experiments prepared in similar and cross-domains. Results depict that least two error rates reported by CCRNet are 1.06% and 1.21% that enhances the benchmark for the state of the arts. This is due to fast convergence and rapid weights updation achieved from convolution and residual connections, respectively. It helps in recognizing the micro-patterns existing within the iris region and results in better feature discrimination among large numbers of iris subjects.

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Authors and Affiliations

  1. Department of Computer Science Engineering, DSPM IIIT Naya Raipur, Naya Raipur, Chhattisgarh, 493661, India

    Meenakshi Choudhary, Vivek Tiwari & U Venkanna

Authors
  1. Meenakshi Choudhary
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  2. Vivek Tiwari
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  3. U Venkanna
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Correspondence to Vivek Tiwari.

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Appendix-I

Appendix-I

Table 12. The statistical data required for Friedman test in terms of AUCs reported by three proposed models for 11 different experiments. The ranks given within parenthesis are provided based on the increasing AUC values for each experiment
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Table 13. The values of AUC reported by the proposed CCRNet and existing deep learning based Models for 11 different experiments
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Table 14. The values of AUC reported by the proposed CCRNet and existing handcrafted methods for 11 different experiments
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Choudhary, M., Tiwari, V. & Venkanna, U. CCRNet: a novel data-driven approach to improve cross-domain Iris recognition. Multimed Tools Appl 79, 32807–32831 (2020). https://doi.org/10.1007/s11042-020-09286-7

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  • DOI: https://doi.org/10.1007/s11042-020-09286-7

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