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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.
In this study, emotional recognition process was carried out using deep learning methods for seven different facial expressions with the data set (RidNet) created using open-access images from the internet. Later, transfer learning was done through RidNet with the renowned evolutionary nerve network architecture in literature such as AlexNet, GoogLeNet and ResNet101. Compound Facial Expressions of Emotion (CE) and Static Facial Expressions in the Wild (SFEW) data sets are defined as test data sets. The evolutionary nerve network architecture, which has shown the best classification performance with the first experimental studies, has been determined. This evolutionary nerve network is trained with AffectNet, The Karolinska Directed Emotional Faces (KDEF) and RidNet. Similar classification achievements have been achieved when trained with AffectNet, KDEF and RidNet networks are tested with a data set (CE) created in a controlled environment. In the test data set in uncontrolled environments (SFEW), the network trained with RidNet has made a clear advantage over other networks.
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.
Field : Fen Bilimleri ve Matematik; Mühendislik
Journal Type : Ulusal
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