EVALUASI PENGENALAN EMOSI PADA CITRA WAJAH BERBASIS ATTENTIVE DEEP CONVOLUTIONAL NEURAL NETWORK

• NUR AINI
• 14002614

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ABSTRAK

 

ABSTRAK Nama : Nur Aini NIM : 14002614 Program Studi : Ilmu Komputer Fakultas : Teknologi Informasi Jenjang : Strata Dua (S2) Konsentrasi : Image Processing Judul Tesis : “Evaluasi Pengenalan Emosi Pada Citra Wajah Berbasis Attentive Deep Convolutional Neural Network” Deteksi emosi pada citra wajah merupakan topik yang mendapat banyak perhatian. Penelitian mengenai pengenalan emosi pada citra wajah masih terus mendapatkan tantangan seperti heteroginitas wajah manusia dan variasi citra wajah pada keadaan sehari-hari. Dari semua teknik yang telah digunakan metode Deep Convolution Neural Network (DCNN) melaporkan peningkatan yang signifikan dibanding metode machine learning tradisional. Dalam beberapa tahun terakhir, mekanisme self-attnetion telah meningkat popularitasnya dalam DCNN, terutama di bidang Natural Language Processing. Pada penelitian ini peneliti menggunakan motode
self attentive deep convolutional neural network dalam pengenalan emosi. FER- 2013 merupakan dataset pengenalan ekspresi wajah terkenal yang diperkenalkan pada International Conference of Machine Learning (ICML) tahun 2013, dataset FER-2013 terdiri dari citra wajah hitam putih berukuran 48x48 piksel dengan jumlah dataset masing-masing untuk training, validasi dan test sebanyak 28.709, 3.589, dan 3.589 sampel. Dataset FER-2013 memiliki 7 kelas yang mewakili emosi dasar manusia yaitu angry (marah), disgust (jijik), fear (takut), happy (senang), sad (sedih) dan surprise (terkejut) dalam kondisi natural sehingga memiliki tantangan dalam pendeteksiannya. Pada penelitian ini, peneliti menggunakan metode attentive deep convolutional neural netwok untuk mengenali emosi pada citra wajah. Dengan menggabungkan mekanisme self-attention ke dalam arsitektur model, jaringan mampu untuk mengenali bagian wajah yang paling relevan untuk setiap emosi, meningkatkan akurasi dan interpretasi model. Penelitian ini melaporkan hasil akurasi sebesar 69.55% yang mana lebih tinggi 11.15% dibandingkan model DCNN tradisional. Kata kunci : Pengenalan Emosi, Citra Wajah, Self-Attention, Deep Convolutional Neural Network.
 

KATA KUNCI

Attentive Deep Convolutional Neural Network

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DAFTAR PUSTAKA

 

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