  Currently, image-text-driven multi-modal deep learning models have
demonstrated their outstanding potential in many fields. In practice, tasks
centered around facial images have broad application prospects. This paper
presents \textbf{FaceCaption-15M}, a large-scale, diverse, and high-quality
dataset of facial images accompanied by their natural language descriptions
(facial image-to-text). This dataset aims to facilitate a study on
face-centered tasks. FaceCaption-15M comprises over 15 million pairs of facial
images and their corresponding natural language descriptions of facial
features, making it the largest facial image-caption dataset to date. We
conducted a comprehensive analysis of image quality, text naturalness, text
complexity, and text-image relevance to demonstrate the superiority of
FaceCaption-15M. To validate the effectiveness of FaceCaption-15M, we first
trained a facial language-image pre-training model (FLIP, similar to CLIP) to
align facial image with its corresponding captions in feature space.
Subsequently, using both image and text encoders and fine-tuning only the
linear layer, our FLIP-based models achieved state-of-the-art results on two
challenging face-centered tasks. The purpose is to promote research in the
field of face-related tasks through the availability of the proposed
FaceCaption-15M dataset. All data, codes, and models are publicly available.
https://huggingface.co/datasets/OpenFace-CQUPT/FaceCaption-15M
