  In recent years, palmprints have been widely used for individual
verification. The rich privacy information in palmprint data necessitates its
protection to ensure security and privacy without sacrificing system
performance. Existing systems often use cancelable technologies to protect
templates, but these technologies ignore the potential risk of data leakage.
Upon breaching the system and gaining access to the stored database, a hacker
could easily manipulate the stored templates, compromising the security of the
verification system. To address this issue, we propose a dual-level cancelable
palmprint verification framework in this paper. Specifically, the raw template
is initially encrypted using a competition hashing network with a first-level
token, facilitating the end-to-end generation of cancelable templates.
Different from previous works, the protected template undergoes further
encryption to differentiate the second-level protected template from the
first-level one. The system specifically creates a negative database (NDB) with
the second-level token for dual-level protection during the enrollment stage.
Reversing the NDB is NP-hard and a fine-grained algorithm for NDB generation is
introduced to manage the noise and specified bits. During the verification
stage, we propose an NDB matching algorithm based on matrix operation to
accelerate the matching process of previous NDB methods caused by
dictionary-based matching rules. This approach circumvents the need to store
templates identical to those utilized for verification, reducing the risk of
potential data leakage. Extensive experiments conducted on public palmprint
datasets have confirmed the effectiveness and generality of the proposed
framework. Upon acceptance of the paper, the code will be accessible at
https://github.com/Deep-Imaging-Group/NPR.
