Iris and periocular recognition in arabian race horses using deep convolutional neural networks

TL;DR

This study explores using deep convolutional neural networks to recognize Arabian race horses through iris and periocular features, aiming for fast, reliable, and non-invasive identification methods.

Contribution

It introduces a fusion approach of iris and periocular features using DCNNs, reducing the need for complex algorithms and fine-tuning for horse biometric recognition.

Findings

Achieved an EER of 9.5% with the proposed method.

Demonstrated robustness to rotation, translation, and image quality variations.

Validated the effectiveness of deep learning for horse biometric identification.

Abstract

This paper presents a study devoted to recognizing horses by means of their iris and periocular features using deep convolutional neural networks (DCNNs). Identification of race horses is crucial for animal identity confirmation prior to racing. As this is usually done shortly before a race, fast and reliable methods that are friendly and inflict no harm upon animals are important. Iris recognition has been shown to work with horse irides, provided that algorithms deployed for such task are fine-tuned for horse irides and input data is of very high quality. In our work, we examine a possibility of utilizing deep convolutional neural networks for a fusion of both iris and periocular region features. With such methodology, ocular biometrics in horses could perform well without employing complicated algorithms that require a lot of fine-tuning and prior knowledge of the input image, while at the same time being rotation, translation, and to some extent also image quality invariant. We were able to achieve promising results, with EER=9.5% using two network architectures with score-level fusion.