# PetFace: A Large-Scale Dataset and Benchmark for Animal Identification

arXiv: https://arxiv.org/abs/2407.13555 · HTML: https://mlsanity.com/paper/2407.13555/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 68 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [WebFace260M: A Benchmark Unveiling the Power of Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2103.04098/) (2021)
- [Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"](https://mlsanity.com/paper/1908.07201/) (2019)
- [WildlifeDatasets: An open-source toolkit for animal re-identification](https://mlsanity.com/paper/2311.09118/) (2023)
- [AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces](https://mlsanity.com/paper/1909.04951/) (2019)
- [Visual Identification of Individual Holstein-Friesian Cattle via Deep Metric Learning](https://mlsanity.com/paper/2006.09205/) (2020)
- [SeaTurtleID2022: A long-span dataset for reliable sea turtle re-identification](https://mlsanity.com/paper/2211.10307/) (2022)
- [NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation](https://mlsanity.com/paper/2005.13359/) (2020)
- [SealID: Saimaa ringed seal re-identification dataset](https://mlsanity.com/paper/2206.02260/) (2022)
- [Towards Self-Supervision for Video Identification of Individual Holstein-Friesian Cattle: The Cows2021 Dataset](https://mlsanity.com/paper/2105.01938/) (2021)
- [A Dataset and Application for Facial Recognition of Individual Gorillas in Zoo Environments](https://mlsanity.com/paper/2012.04689/) (2020)
- [OpenAnimalTracks: A Dataset for Animal Track Recognition](https://mlsanity.com/paper/2406.09647/) (2024)
- [Where Is My Puppy? Retrieving Lost Dogs by Facial Features](https://mlsanity.com/paper/1510.02781/) (2015)
- [Automated Detection of Cat Facial Landmarks](https://mlsanity.com/paper/2310.09793/) (2023)

## Built on by (most cited first, 16 of 16)

- [Flow Matching with Semidiscrete Couplings](https://mlsanity.com/paper/2509.25519/) (2025)
- [Unsupervised Pelage Pattern Unwrapping for Animal Re-identification](https://mlsanity.com/paper/2506.15369/) (2025)
- [Self-supervised Learning on Camera Trap Footage Yields a Strong Universal Face Embedder](https://mlsanity.com/paper/2507.10552/) (2025)
- [Generalizable Object Re-Identification via Visual In-Context Prompting](https://mlsanity.com/paper/2508.21222/) (2025)
- [AnimalClue: Recognizing Animals by their Traces](https://mlsanity.com/paper/2507.20240/) (2025)
- [FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields](https://mlsanity.com/paper/2508.06301/) (2025)
- [IIR-VLM: In-Context Instance-level Recognition for Large Vision-Language Models](https://mlsanity.com/paper/2601.14188/) (2026)
- [Categorical Keypoint Positional Embedding for Robust Animal Re-Identification](https://mlsanity.com/paper/2412.00818/) (2024)
- [PawPrint: Whose Footprints Are These? Identifying Animal Individuals by Their Footprints](https://mlsanity.com/paper/2505.17445/) (2025)
- [A solution to generalized learning from small training sets found in infants repeated visual experiences of individual objects](https://mlsanity.com/paper/2510.15060/) (2025)
- [Motion Transfer-Enhanced StyleGAN for Generating Diverse Macaque Facial Expressions](https://mlsanity.com/paper/2511.16711/) (2025)
- [Not All Birds Look The Same: Identity-Preserving Generation For Birds](https://mlsanity.com/paper/2512.04485/) (2025)
- [From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification](https://mlsanity.com/paper/2603.02270/) (2026)
- [BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment](https://mlsanity.com/paper/2603.23883/) (2026)
- [Beyond Humans: Multispecies Animal Face Recognition Using Transfer Learning](https://mlsanity.com/paper/2606.09353/) (2026)
- [Cross-Species Animal Re-Identification with Semantic Consistency Learning](https://mlsanity.com/paper/2609.09705/) (2026)

## Abstract (arXiv)

Automated animal face identification plays a crucial role in the monitoring of behaviors, conducting of surveys, and finding of lost animals. Despite the advancements in human face identification, the lack of datasets and benchmarks in the animal domain has impeded progress. In this paper, we introduce the PetFace dataset, a comprehensive resource for animal face identification encompassing 257,484 unique individuals across 13 animal families and 319 breed categories, including both experimental and pet animals. This large-scale collection of individuals facilitates the investigation of unseen animal face verification, an area that has not been sufficiently explored in existing datasets due to the limited number of individuals. Moreover, PetFace also has fine-grained annotations such as sex, breed, color, and pattern. We provide multiple benchmarks including re-identification for seen individuals and verification for unseen individuals. The models trained on our dataset outperform those trained on prior datasets, even for detailed breed variations and unseen animal families. Our result also indicates that there is some room to improve the performance of integrated identification on multiple animal families. We hope the PetFace dataset will facilitate animal face identification and encourage the development of non-invasive animal automatic identification methods.