Palmprint presentation attack detection (PAD) is critical for securing biometric systems, yet existing methods often suffer from poor across-domain generalization due to insufficient exploration of intrinsic physical attributes in palmprint images, leading to over-sensitivity to domain-specific noise such as lighting variations and background artifacts. In this paper, we introduce method to learn robust image priors from multi-dimensional perspectives. Our method promotes texture-focused representations via high-frequency residual reconstruction while suppressing sensitivity to absolute color through masked autoencoding with a chromaticity-distribution alignment regularizer. This enables our proposed method to effectively capture the intrinsic physical priors from raw palmprint images, thereby discriminating genuine palmprint images from attacks. After this pre-training stage, only the shared encoder is retained and fine-tuned for downstream PAD tasks. Experimental results across seven domains demonstrate that HiChrom-MAE significantly improves cross-medium reliability and outperforms state-of-the-art methods.

One of the primary challenges in achieving Infrared-Visible Person Re-Identification (IV Re-ID) is the significant differences in modalities between visible (VIS) and infrared (IR) images.In addressing this challenge, we propose a new data augmentation method-SOFTCUTMIX and introduce a new algorithm called SOFTCUTMIX Auxiliary Modality(SCAM). SOFTCUTMIX augmentation strategy aims to randomly crop and blend portions of two images with random weights, and meanwhile blend their non-cropped portions with other random weights. SCAM algorithm generates mixed modality images by blending visible light and infrared images and serves as an auxiliary modality to reduce the inherent modality differences. We also design a Channel Random Selection (CRS) to adjust the channels of the three-channel visible light image to reduce differences with the single-channel infrared image. Furthermore, we propose a Weighted Regularization Center Triplet Loss (WRCT) and combine it with the Weighted Regularization Triplet Loss (WRT). This approach reduces intra-class variations and increases inter-class separability, thereby enhancing the discriminative power of the learned features. Experimental results on the SYSU-MM01 and RegDB datasets demonstrate that our algorithm significantly outperforms the state-of-the-art method.

In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.