Text-Based Face Retrieval: Methods and Challenges

Previous researches on face retrieval have concentrated on using image-based queries. In this paper, we focus on the task of retrieving faces from a database based on queries given as texts, which holds significant potential for practical applications in public security and multimedia. Our approach employs a vision-language pre-training model as the backbone, effectively incorporating contrastive learning, image-text matching learning, and masked language modeling tasks. Furthermore, it employs a coarse-to-fine retrieval strategy to enhance the accuracy of text-based face retrieval. We present CelebA-Text-Identity dataset, comprising of 202,599 facial images of 10,178 unique identities, each paired with an accompanying textual description. The experimental results we obtained on CelebA-Text-Identity demonstrate the inherent challenges of text-based face retrieval. We expect that our proposed benchmark will encourage the advancement of biometric retrieval techniques and expand the range of applications for text-image retrieval technology.