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Lifelong iris presentation attack detection without forgetting

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Abstract

Despite the promising results achieved by deep iris presentation attack detection (PAD) in dataset-specific scenarios, the advanced approach remains vulnerable to novel attacks. Real-world attacks evolve over time. Typically, fine-tuning and retraining from scratch are employed to incrementally learn new attacks. However, fine-tuning degrades performance on old attacks, i.e., catastrophic forgetting. Retraining on all data is unavailable due to data privacy. To address these issues, we are the first to propose a lifelong iris PAD to incrementally learn new attacks without storing old data. Our approach utilizes a prompt pool to preserve attack-independent and attack-shared knowledge, wherein learnable prompts aid in prediction by the pre-trained Vision Transformer (ViT). Furthermore, adaptive attention masks for sequential new attacks are applied to pre-trained ViT. Consequently, our method improves plasticity while preserving stability. Extensive experiments are performed on our building dataset combing IITD and CASIA to evaluate iris PAD in incremental learning. Our proposed method obtains competitive performance over state-of-the-art Iris PAD schemes.

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Acknowledgments

This research was funded by the National Natural Science Foundation of China (Grant Number 61471181), the Natural Science Foundation of Jilin Province (Grant Number YDZJ202101ZYTS144), the Jilin Province Industrial Innovation Special Fund Project (Grant Number 2019C053-2), and the Science and Technology Project of the Jilin Provincial Education Department (Grant Number JJKH20180448KJ). Thanks to the Jilin Provincial Key Laboratory of Biometrics New Technology for supporting this research.

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Zhiyong Zhou, Yuanning Liu, Xiaodong Zhu, Shuai Liu & Shaoqiang Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Zhiyong Zhou, Yuanning Liu, Xiaodong Zhu, Shuai Liu & Shaoqiang Zhang

  3. College of Biological and Agricultural Engineering, Jilin University, Changchun, 130022, China

    Yuanfeng Li

  4. Graduate School of Engineering, Nagasaki Institute of Applied Science, Nagasaki, 851-0193, Japan

    Zhen Liu

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  1. Zhiyong Zhou
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ZZ wrote the main manuscript text. YL, XZ, and SL provided the main ideas and methods. SZ and YL prepared figures and tables. ZL corrected grammar and formatting. All authors reviewed the manuscript.

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Correspondence to Xiaodong Zhu.

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Zhou, Z., Liu, Y., Zhu, X. et al. Lifelong iris presentation attack detection without forgetting. J Supercomput 80, 1–19 (2024). https://doi.org/10.1007/s11227-023-05445-3

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  • DOI: https://doi.org/10.1007/s11227-023-05445-3

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