Estimasi Arah Tatapan Mata Menggunakan Ensemble Convolutional Neural Network
Abstract
Studi arah tatapan mata adalah salah satu masalah dalam bidang computer vision. Pengetahuan akan arah tatapan mata dapat memberikan informasi berharga yang dapat dimanfaatkan untuk berbagai macam keperluan dalam bidang lainnya, khususnya dalam bidang interaksi manusia dengan komputer. Dalam paper ini nantinya akan meneliti arah tatapan mata menggunakan Ensemble Convolutional Neural Network dengan menggunakan dataset CAVE (Columbia Gaze Dataset). Convolutional Neural Netwok (CNN) merupakan sebuah bidang keilmuan dalam bidang machine learning yang berkembang cukup pesat khususnya untuk mengklasifikasi citra. Nantinya, paper ini akan menganalisa dan membandingkan hasil F1 score dan weighted kappa (w-kappa) score serta error dari klasifikasi dengan menggunakan 3, 9, dan 21 kelas. Dengan sama-sama menggunakan kanal RGB sebagai gambar input, maka dapat dibandingkan dan disimpulkan bahwa dengan menggunakan metode Ensemble Convolutional Neural Network dengan koefisien 1 untuk mata kiri, 1 untuk mata kanan, dan 3 untuk kedua mata untuk klasifikasi dengan 3 dan 9 kelas, serta dengan koefisien 1 untuk mata kiri, 1 untuk mata kanan, dan 5 untuk kedua mata untuk klasifikasi dengan 21 kelas dapat menghasilkan hasil F1 score dan w-kappa yang lebih baik, serta tingkat error yang lebih rendah daripada menggunakan koefisien dengan nilai lainnya.
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