# Markerless retro-identification complements re-identification of individual insect subjects in archived image data of biological experiments

arXiv: https://arxiv.org/abs/2405.13376 · HTML: https://mlsanity.com/paper/2405.13376/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 6 of 6)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [An Enhanced Deep Feature Representation for Person Re-identification](https://mlsanity.com/paper/1604.07807/) (2016)
- [Tracking all members of a honey bee colony over their lifetime](https://mlsanity.com/paper/1802.03192/) (2018)
- [Towards Automatic Honey Bee Flower-Patch Assays with Paint Marking Re-Identification](https://mlsanity.com/paper/2311.07407/) (2023)
- [Automatic detection and decoding of honey bee waggle dances](https://mlsanity.com/paper/1708.06590/) (2017)

## Built on by (most cited first, 1 of 1)

- [AGRICAM: A Track-Mounted Crop Pollination Monitoring Robot](https://mlsanity.com/paper/2608.29237/) (2026)

## Abstract (arXiv)

This study introduces markerless retro-identification of animals, a novel concept and practical technique to identify past occurrences of organisms in archived data, that complements traditional forward-looking chronological re-identification methods in longitudinal behavioural research. Identification of a key individual among multiple subjects may occur late in an experiment if it reveals itself through interesting behaviour after a period of undifferentiated performance. Often, longitudinal studies also encounter subject attrition during experiments. Effort invested in training software models to recognise and track such individuals is wasted if they fail to complete the experiment. Ideally, we would be able to select individuals who both complete an experiment and/or differentiate themselves via interesting behaviour, prior to investing computational resources in training image classification software to recognise them. We propose retro-identification for model training to achieve this aim. This reduces manual annotation effort and computational resources by identifying subjects only after they differentiate themselves late, or at an experiment's conclusion. Our study dataset comprises observations made of morphologically similar reed bees (textitExoneura robusta) over five days. We evaluated model performance by training on final day five data, testing on the sequence of preceding days, and comparing results to the usual chronological evaluation from day one. Results indicate no significant accuracy difference between models. This underscores retro-identification's value in improving resource efficiency in longitudinal animal studies.