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Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

TMacaque-FaceNet: Automatic Facial Recognition Based on Vision Transformer for Wild Tibetan Macaques.

Qiyang Gao^1,2, Lele Zhang^1,2, He Luo^1,2

¹School of Life Sciences and Medical Engineering, Anhui University, Hefei 230601, China.

Animals : an Open Access Journal From MDPI

|April 14, 2026

Summary

This study introduces TMacaque-FaceNet, an automated system for recognizing individual Tibetan macaques using deep learning. The AI accurately identifies macaques from images, aiding non-invasive wildlife monitoring and behavioral studies.

Keywords:

Tibetan macaque YOLO individual recognition vision transformer

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Area of Science:

Behavioral Ecology
Conservation Biology
Artificial Intelligence

Background:

Individual recognition is crucial for studying wild social animals in behavioral ecology and conservation.
Traditional methods like tagging are invasive and require extensive field experience.
Deep learning offers non-invasive recognition but faces challenges with species-specific traits, environment, and data size.

Purpose of the Study:

To develop and validate an automated, non-invasive individual recognition system for wild Tibetan macaques (Macaca thibetana).
To integrate deep learning models for accurate face detection and individual classification.
To provide a practical tool for automated behavioral observation and population monitoring.

Main Methods:

Utilized 3385 images of 18 identified wild Tibetan macaques.
Developed TMacaque-FaceNet, combining YOLO for face detection and Vision Transformer (ViT) for classification.
Performed gradient-weighted attention rollout analysis for model interpretability.

Main Results:

The Tibetan macaque face detector achieved high performance (mAP@0.5: 0.971, precision: 0.974, recall: 0.931).
The individual recognizer achieved a top-1 accuracy of 96.33% on the test set and 95.56% on a temporal validation set.
Attention analysis confirmed the model focused on biologically relevant facial features.

Conclusions:

TMacaque-FaceNet provides an effective, automated method for non-invasive individual recognition of Tibetan macaques.
This technology facilitates automated behavioral observation, social network analysis, and long-term population monitoring.
The study demonstrates the potential of deep learning for advancing wildlife research and conservation efforts.