TY  - THES
N1  - Pembimbing: Prof. Dr. Ir. Shofwatul Uyun, ST, M.Kom.
ID  - digilib68754
UR  - https://digilib.uin-suka.ac.id/id/eprint/68754/
A1  - Nida Muhliya Barkah, NIM.: 22206052003
Y1  - 2024/08/27/
N2  - Facial expressions are essential in non-verbal communication, as they naturally convey human emotions during personal interactions. Emotion recognition from facial expressions through computer vision is a popular topic in affective computing. The swift advancement of deep learning has resulted in its growing application in facial emotion recognition. However, it still encounters a significant challenge due to the necessity for a substantial amount of data to operate effectively. Numerous studies have utilized transfer learning to tackle this problem, yet a standardized approach for implementing transfer learning in facial emotion recognition has not been established. This research classifies facial emotion images into seven categories using three transfer learning models: ResNet-50, Xception, and Inception V3 on the FER-2013 dataset. Various experiments, including data pre-processing, hyperparameter tuning, and model training techniques, have been conducted. The data pre-processing results show that each model requires different input image sizes to achieve the best accuracy. Hyperparameter tuning results indicate accuracy improvements of 6.3527% for ResNet-50, 4.6949% for Inception V3, and 1.039% for Xception. Augmentation experiments show that augmenting only the disgust class yields better accuracy than augmenting all classes. Model training experiments reveal that the freeze fine-tuning method is not better compared to the fine-tuning method on datasets with thousands of samples but is superior to the freeze layer method commonly used in many studies. The best accuracy values for each model are 64.8927% for ResNet-50, 65.8261% for Xception, and 66.3973% for Inception V3.
PB  - UIN SUNAN KALIJAGA YOGYAKARTA
KW  - Ekspresi Wajah
KW  -  Pengenalan Emosi
KW  -  Deep Learning
KW  -  Dataset FER-2013
KW  -  ResNet-50
KW  -  Xception
KW  -  InceptionV3
KW  -  Klasifikasi
M1  - masters
TI  - OPTIMASI PADA TRANSFER LEARNING UNTUK KLASIFIKASI EMOSI WAJAH PADA DATASET FER-2013
AV  - restricted
EP  - 80
ER  -