# Deep Learning for Automatic Detection and Facial Recognition in Japanese Macaques: Illuminating Social Networks

arXiv: https://arxiv.org/abs/2310.06489 · HTML: https://mlsanity.com/paper/2310.06489/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 69 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 1 of 1)

- [Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging](https://mlsanity.com/paper/1912.03606/) (2019)

## Built on by (most cited first, 3 of 3)

- [BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes](https://mlsanity.com/paper/2411.06896/) (2024)
- [Computer Vision for Primate Behavior Analysis in the Wild](https://mlsanity.com/paper/2401.16424/) (2024)
- [Computer Vision for Wildlife Monitoring: Detecting Brown Howler Monkeys using YOLO](https://mlsanity.com/paper/2607.01396/) (2026)

## Abstract (arXiv)

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offered new methodological perspectives through automatization of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identifications done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching a 82.2% accuracy and (ii) the creation of an individual recognizer for K=ojima island macaques population (YOLOv8n model), reaching a 83% accuracy. We also created a K=ojima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this innovative approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.