# Cattle Identification Using Muzzle Images and Deep Learning Techniques

arXiv: https://arxiv.org/abs/2311.08148 · HTML: https://mlsanity.com/paper/2311.08148/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Iris Recognition Under Biologically Troublesome Conditions - Effects of Aging, Diseases and Post-mortem Changes](https://mlsanity.com/paper/1809.00182/) (2018)

## Built on by (most cited first, 2 of 2)

- [CCoMAML: Efficient Cattle Identification Using Cooperative Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2509.11219/) (2025)
- [Cross-Breed Pig Identification Using Auricular Vein Pattern Recognition: A Machine Learning Approach for Small-Scale Farming Applications](https://mlsanity.com/paper/2510.02197/) (2025)

## Abstract (arXiv)

Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require specialized equipment and are susceptible to attacks. Biometric identification using time-immutable dermatoglyphic features such as muzzle prints and iris patterns is a promising solution. This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. Two deep learning classification models are implemented - wide ResNet50 and VGG16BN and image compression is done to lower the image quality and adapt the models to work for the African context. From the experiments run, a maximum accuracy of 99.5% is achieved while using the wide ResNet50 model with a compression retaining 25% of the original image. From the study, it is noted that the time required by the models to train and converge as well as recognition time are dependent on the machine used to run the model.