Improving Person Recognition for Single-Person-in-Photos: Intimacy in Photo Collections

Person recognition in photo collections is a critical yet challenging task in computer vision. Previous studies have used social relationships within photo collections to address this issue. However, these methods often fail when performing single-person-in-photos recognition in photo collections, as they cannot rely on social connections for recognition. In this work, we discard social relationships and instead measure the relationships between photos to solve this problem. We designed a new model that includes a multi-parameter attention network for adaptively fusing visual features and a unified formula for measuring photo intimacy. This model effectively recognizes individuals in single photo within the collection. Due to outdated annotations and missing photos in the existing PIPA (Person in Photo Album) dataset, we manually re-annotated it and added approximately ten thousand photos of Asian individuals to address the underrepresentation issue. Our results on the re-annotated PIPA dataset are superior to previous studies in most cases, and experiments on the supplemented dataset further demonstrate the effectiveness of our method. We have made the PIPA dataset publicly available on Zenodo, with the DOI: (accessed on 15 October 2025).