# KaoKore: A Pre-modern Japanese Art Facial Expression Dataset

arXiv: https://arxiv.org/abs/2002.08595 · HTML: https://mlsanity.com/paper/2002.08595/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 18 of 18)

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- [Active Prompt Learning with Vision-Language Model Priors](https://mlsanity.com/paper/2411.16722/) (2024)
- [Level of agreement between emotions generated by Artificial Intelligence and human evaluation: a methodological proposal](https://mlsanity.com/paper/2410.08332/) (2024)
- [Predicting the Ordering of Characters in Japanese Historical Documents](https://mlsanity.com/paper/2106.06786/) (2021)
- [Transferability of Representations Learned using Supervised Contrastive Learning Trained on a Multi-Domain Dataset](https://mlsanity.com/paper/2309.15486/) (2023)
- [ST-SACLF: Style Transfer Informed Self-Attention Classifier for Bias-Aware Painting Classification](https://mlsanity.com/paper/2408.01827/) (2024)

## Abstract (arXiv)

From classifying handwritten digits to generating strings of text, the datasets which have received long-time focus from the machine learning community vary greatly in their subject matter. This has motivated a renewed interest in building datasets which are socially and culturally relevant, so that algorithmic research may have a more direct and immediate impact on society. One such area is in history and the humanities, where better and relevant machine learning models can accelerate research across various fields. To this end, newly released benchmarks and models have been proposed for transcribing historical Japanese cursive writing, yet for the field as a whole using machine learning for historical Japanese artworks still remains largely uncharted. To bridge this gap, in this work we propose a new dataset KaoKore which consists of faces extracted from pre-modern Japanese artwork. We demonstrate its value as both a dataset for image classification as well as a creative and artistic dataset, which we explore using generative models. Dataset available at https://github.com/rois-codh/kaokore