Pupil Center Localization Based on Mini U-Net

Year 2022, Volume: IDAP-2022 : International Artificial Intelligence and Data Processing Symposium , 185 - 191, 10.10.2022

Kenan Donuk , Davut Hanbay

https://doi.org/10.53070/bbd.1173482

Cited By: 1

Abstract

Many methods have been used from past to present to determine the location of the pupil center, which has an important place in eye tracking algorithms. These methods are usually shape-feature and appearance-based. Shape-feature-based methods use morphological image processing techniques, invariant geometric features of the eye, and infrared light to locate the iris and pupil. These methods are affected by real world conditions such as light, low resolution. In contrast, appearance-based methods are less sensitive to these conditions. In this study, Mini U-Net network, which is one of the appearance-based methods that automatically learns eye features and performs pupil center localization, is proposed. The proposed network was evaluated using the publicly available GI4E dataset for pupil center localization. In the test results of the network, measurements were made according to the maximum normalized error criterion. Accordingly, the center of the pupil was determined with an accuracy of 98.40%. The proposed network is compared with the latest technological methods and the performance of the proposed network is shown.

Keywords

U-Net, mini U-Net, pupil center localization, gi4e.

Pupil Center Localization Based on Mini U-Net

Abstract

Göz takip algoritmalarında önemli bir yere sahip olan göz bebeği merkezinin yerini belirlemek için geçmişten günümüze birçok yöntem kullanılmıştır. Bu yöntemler genellikle şekil-özellik ve görünüm temellidir. Şekil-özellik tabanlı yöntemler, iris ve göz bebeğinin yerini belirlemek için morfolojik görüntü işleme tekniklerini, gözün değişmez geometrik özelliklerini ve kızılötesi ışığı kullanır. Bu yöntemler ışık, düşük çözünürlük gibi gerçek dünya koşullarından etkilenir. Buna karşılık, görünüm temelli yöntemler bu koşullara daha az duyarlıdır. Bu çalışmada, göz özelliklerini otomatik olarak öğrenen ve göz bebeği merkezi lokalizasyonu gerçekleştiren görünüm tabanlı yöntemlerden biri olan Mini U-Net ağı önerilmiştir. Önerilen ağ, göz bebeği merkezi yerelleştirmesi için halka açık GI4E veri seti kullanılarak değerlendirildi. Ağın test sonuçlarında maksimum normalize edilmiş hata kriterine göre ölçümler yapılmıştır. Buna göre göz bebeğinin merkezi %98,40 doğrulukla belirlendi. Önerilen ağ, en son teknolojik yöntemlerle karşılaştırılmış ve önerilen ağın performansı ortaya konmuştur.

Keywords

U-Net, mini U-Net, Göz bebeği merkezi yerelleştirmesi, gi4e

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