IFViT: Interpretable Fixed-Length Representation for Fingerprint
  Matching via Vision Transformer
Yuhang Qiu Honghui Chen Xingbo Dong Zheng Lin Iman Yi Liao Massimo Tistarelli Zhe Jin
Determining dense feature points on fingerprints used in constructing deep
fixed-length representations for accurate matching, particularly at the pixel
level, is of significant interest. To explore the interpretability of
fingerprint matching, we propose a multi-stage interpretable fingerprint
matching network, namely Interpretable Fixed-length Representation for
Fingerprint Matching via Vision Transformer (IFViT), which consists of two
primary modules. The first module, an interpretable dense registration module,
establishes a Vision Transformer (ViT)-based Siamese Network to capture
long-range dependencies and the global context in fingerprint pairs. It
provides interpretable dense pixel-wise correspondences of feature points for
fingerprint alignment and enhances the interpretability in the subsequent
matching stage. The second module takes into account both local and global
representations of the aligned fingerprint pair to achieve an interpretable
fixed-length representation extraction and matching. It employs the ViTs
trained in the first module with the additional fully connected layer and
retrains them to simultaneously produce the discriminative fixed-length
representation and interpretable dense pixel-wise correspondences of feature
points. Extensive experimental results on diverse publicly available
fingerprint databases demonstrate that the proposed framework not only exhibits
superior performance on dense registration and matching but also significantly
promotes the interpretability in deep fixed-length representations-based
fingerprint matching.