Generating Stylized Features for Single-Source Cross-Dataset Palmprint Recognition With Unseen Target Dataset

As a promising topic in palmprint recognition, cross-dataset palmprint recognition is attracting more and more research interests. In this paper, a more difficult yet realistic scenario is studied, i.e., Single-Source Cross-Dataset Palmprint Recognition with Unseen Target dataset (S2CDPR-UT). It is aimed to generalize a palmprint feature extractor trained only on a single source dataset to multiple unseen target datasets collected by different devices or environments. To combat this challenge, we propose a novel method to improve the generalization of feature extractor for S2CDPR-UT, named Generating stylIzed FeaTures (GIFT). Firstly, the raw features are decoupled into high- and low- frequency components. Then, a feature stylization module is constructed to perturb the mean and variance of low-frequency components to generate more stylized features, which can provided more valuable knowledge. Furthermore, two diversity enhancement and consistency preservation supervisions are introduced at feature level to help to learn the model. The former is aimed to enhance the diversity of stylized features to expand the feature space. Meanwhile, the later is aimed to maintain the semantic consistency to ensure accurate palmprint recognition. Extensive experiments carried out on CASIA Multi-Spectral, XJTU-UP, and MPD palmprint databases show that our GIFT method can achieve significant improvement of performance over other methods. The codes will be released at
https://github.com/HuikaiShao/GIFT
.