Abstract
The distinct ridge features of palmvein and palmprint images, among other palm-related images, make them vulnerable to reversible attacks that can reconstruct the original structure, leading to permanent leakage of biometric features. Additionally, existing multi-modal template protection schemes treat the feature data of each modality as independent, failing to fully capture the inter-modality correlation. Therefore, this paper proposes a multi-modal shared biometric key generation network called PalmKeyNet. By designing keys unrelated to the original palm images as biometric templates, the irreversibility of features is achieved. Additionally, by constructing a multi-modal biometric key generation network, we transform the palm images of different modalities into a unified feature-key space, enhancing the inter-modal correlation. Furthermore, LDPC coding is introduced for multi-modal key error correction to reduce noise interference and improve key discriminability. The proposed approach simultaneously enhances the discriminability, correlation, and security of multi-modal features. The trained PalmKeyNet can be deployed in four modes: single-modal matching (palmprint vs. palmprint and palmvein vs. palmvein), multi-modal matching, and cross-matching. Experimental results on four publicly available palm databases consistently demonstrate the superiority of the proposed method over state-of-the-art approaches.
This work was supported by the Natural Science Foundation for the Higher Education Institutions of Anhui Province (Grant No. 2022AH050091).
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Liu, X., Wang, H., Wang, M., Tao, L. (2024). PalmKeyNet: Palm Template Protection Based on Multi-modal Shared Key. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_9
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DOI: https://doi.org/10.1007/978-981-99-8469-5_9
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