Deteksi Kedipan dengan Metode CNN dan Percentage of Eyelid Closure (PERCLOS)

Abstract

Pengembangan teknologi mengenai face detection dan eyes detection melaju sangat pesat, sehingga peneliti berlomba-lomba meneliti metode dan algoritma yang optimal untuk pengaplikasian di kehidupan sehari-hari, mulai dari pengamanan biometrics sampai identifikasi wajah secara au- tomasi. Di tugas akhir ini diusulkan penggunaan metode Convo- lutional Neural Network (CNN) dan Percentage of Eyelid Closure (PERCLOS) pada pendeteksi kedipan mata. Sistem dibangun menggunakan webcam personal computer sebagai kamera dan mendeteksi secara real-time. Sistem dapat mengenali kondisi ketika mata tertutup atau mata terbuka dan menentukan lebar bukaan mata dengan menggunakan Eye Aspect Ratio (EAR) serta dapat mengestimasi skor tatapan dengan menggunakan Percentage of Eyelid Closure (PERCLOS). Sistem dapat menge- nali wajah dari objek bukan wajah dengan jarak pendeteksian optimal antara 40-70 cm. Model hasil training dapat mengk- lasifikasikan kondisi mata terbuka dan mata tertutup dengan menggunakan Convolutional Neural Network dengan arstitektur yang memiliki 3 layer mendapatkan hasil accuracy 98% dan loss 2.05%

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