ConvNet – Iris Recognition in Arabian Race Horses using Deep Convolutional Neural Networks

Authors: Mateusz Trokielewicz, Mateusz Szadkowski (presenting author)
Title: Recognizing Horses by Iris and Periocular Features Using Deep Convolutional Neural Networks - a preliminary study
Abstract: This paper presents a preliminary 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 given race, fast and reliable methods that are friendly and inflict no harm upon animals are important. Typical iris recognition methods, built with human irides in mind, usually employ approximating the iris and pupil boundaries with circular shapes. These approximations are not suitable for horse irides due to significant differences in eye anatomy. However, iris recognition has been shown to work well with horse irides, provided that algorithms deployed for such task are fine-tuned for horse irides. In our work, we examine a possibility of utilizing deep convolutional neural networks for a fusion of both eye and periocular region features. With such methodology in place, 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, hopefully, also image quality invariant.