CI³Former: A Cross-Image Information Interaction Network for Kinship Verification

Kinship verification using facial information determines whether two faces share a familial relationship. Existing methods improve verification by leveraging negative sample information and addressing distribution differences but often extract independent features from parent and child images separately, ignoring variations in pairwise similarity. To overcome this, we propose CI³Former, a Swin-Transformer-based model that enables cross-image information interaction for joint feature extraction. By incorporating a Self-Attention based Interaction (SAI) module within each Swin-Transformer block, our method allows mutual querying between parent and child features, dynamically guiding region-level feature extraction and adaptively focusing on similar regions. Additionally, we introduce a Multi-metric Similarity based Interaction (MSI) module for feature fusion, which processes paired features through similarity measurements before final prediction. The model is trained with contrastive and binary cross-entropy losses to enhance coupled feature learning. Extensive experiments on four kinship verification datasets and a signature verification dataset demonstrate that CI³Former outperforms state-of-the-art methods, showcasing its effectiveness, robustness, and strong cross-task generalization.