Enhancing Facial Expression Recognition in the Wild with Deep Learning Methods Using a New Dataset: RidNet

Year 2019, Volume: 6 Issue: 2, 384 - 396, 26.12.2019

Rıdvan Özdemir Mehmet Koç

https://doi.org/10.35193/bseufbd.645138

Cited By: 5

Abstract

In this study, emotion
recognition process was performed by using deep learning methods for seven
different facial expressions with the data set (RidNet) which was created by
using images that are publicly accessible from internet. Afterwards, transfer
learning over RidNet was done with well-known convolutional neural network
architectures such as AlexNet, GoogLeNet and ResNet101. Compound Facial
Expressions of Emotion (CE) and Static Facial Expressions in the Wild (SFEW)
datasets were determined as test datasets. In the first experimental studies,
convolutional neural network architecture with the best classification
performance was determined. This convolutional neural network was trained with
AffectNet, The Karolinska Directed Emotional Faces and RidNet. Similar
classification performances were achieved when the AffectNet, KDEF, and
RidNet-trained networks were tested with the data set (CE) generated in a controlled
environment. In the test data set (SFEW) in an uncontrolled environment,
RidNet-trained network gave a significant advantage over other networks.

Keywords

Facial Expression Recognition, Deep Learning, Transfer Learning, Convolutional Neural Networks

Project Number

2019-01.BŞEÜ.03-05

Yeni Bir Veri Kümesi (RidNet) Kullanarak Kontrolsüz Ortamda Yüz İfadesi Tanımanın Derin Öğrenme Yöntemleri ile İyileştirilmesi

Abstract

Bu çalışmada, internetten genel erişime açık görüntüler kullanılarak oluşturulan veri kümesi (RidNet) ile yedi farklı yüz ifadesi için derin öğrenme yöntemleri kullanılarak duygu tanıma işlemi yapılmıştır. Daha sonra AlexNet, GoogLeNet ve ResNet101 gibi literatürdeki tanınmış evrişimli sinir ağları mimarileri ile RidNet üzerinden transfer öğrenimi yapılmıştır. Compound Facial Expressions of Emotion (CE) ve Static Facial Expressions in the Wild (SFEW) veri kümeleri test veri kümeleri olarak belirlenmiştir. İlk olarak yapılan deneysel çalışmalar ile en iyi sınıflandırma performansını gösteren evrişimli sinir ağı mimarisi belirlenmiştir. Bu evrişimli sinir ağı AffectNet, The Karolinska Directed Emotional Faces (KDEF) ve RidNet ile eğitilmiştir. AffectNet, KDEF ve RidNet ile eğitilmiş ağlar kontrollü ortamda oluşturulan veri kümesi (CE) ile test edildiğinde benzer sınıflandırma başarımları elde edilmiştir. Kontrolsüz ortamdaki test veri kümesinde (SFEW) ise RidNet ile eğitilen ağ diğer ağlara belirgin bir üstünlük sağlamıştır.

Keywords

Yüz İfadesi Tanıma, Derin Öğrenme, transfer öğrenmesi

Supporting Institution

Bilecik Şeyh Edebali Üniversitei

Project Number

2019-01.BŞEÜ.03-05

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