Unveiling Social Relations: Leveraging Interpersonal Similarity Learning for Social Relation Recognition

Tang, W ORCID: https://orcid.org/0000-0002-6925-9067, Qing, L ORCID: https://orcid.org/0000-0003-3555-0005, Gou, H ORCID: https://orcid.org/0000-0002-7800-6248, Guo, L ORCID: https://orcid.org/0000-0003-1272-8480 and Peng, Y ORCID: https://orcid.org/0000-0002-5508-1819 (2023) Unveiling Social Relations: Leveraging Interpersonal Similarity Learning for Social Relation Recognition. IEEE Signal Processing Letters, 30. pp. 1142-1146. ISSN 1070-9908

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Abstract

Identifying social relationships from images is a challenging yet promising research area with great potential for improving human health and enhancing our understanding of social networks. However, present endeavors in this field tend to concentrate on leveraging visual features for the exploration of social relationships, while disregarding certain concealed information that lies beneath these features, such as interpersonal similarity. These methodologies may result in inadequate visual data encoding, thereby imposing limitations on the accuracy of social relationship recognition. In light of this, we propose a novel framework that utilizes interpersonal similarities within images to provide more information for identifying social relationships, thereby mitigating the issue of insufficient feature exploration. Furthermore, our proposed framework incorporates an innovative CF-Loss function that effectively incentivizes the identification of accurate social relationships while penalizing incorrect identifications, ultimately bolstering the model's capacity to discriminate between distinct social relationships. Our experimental findings demonstrate the superiority of our proposed framework over state-of-the-art methods on public datasets, confirming its effectiveness and accuracy in identifying social relationships.