Arabian Horse Identification Benchmark Dataset

TL;DR

This paper introduces a new benchmark dataset of 300 high-quality color muzzle print images from 50 Arabian horses, designed to facilitate research and development of Arabian horse identification systems under real-world conditions.

Contribution

It provides the first standardized, diverse, and quality-controlled muzzle print dataset for Arabian horse identification research.

Findings

Dataset includes images with various quality levels and degradation factors.

The dataset enables testing of feature extraction and classification algorithms.

It supports benchmarking and comparison of Arabian horse identification methods.

Abstract

The lack of a standard muzzle print database is a challenge for conducting researches in Arabian horse identification systems. Therefore, collecting a muzzle print images database is a crucial decision. The dataset presented in this paper is an option for the studies that need a dataset for testing and comparing the algorithms under development for Arabian horse identification. Our collected dataset consists of 300 color images that were collected from 50 Arabian horse muzzle species. This dataset has been collected from 50 Arabian horses with 6 muzzle print images each. A special care has been given to the quality of the collected images. The collected images cover different quality levels and degradation factors such as image rotation and image partiality for simulating real time identification operations. This dataset can be used to test the identification of Arabian horse system including the extracted features and the selected classifier.