  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.
