Evrişimli Sinir Ağları Kullanılarak Duygu Durum Tespitinin İyileştirilmesi ve Eğitim Verimliliğinin Analizi

Bu çalışmada, yüzdeki duygu ifadelerini tespit etmek için literatürdeki diğer modellerden daha yüksek doğruluk oranına sahip bir evrişimli sinir ağı modeli (CNN) önerilmiştir. Evrişimli sinir ağı modelini eğitmek için yedi duygu kategorisinde insan yüzleri içeren ve 30.000 imge’den oluşan FER2013 veri seti kullanılmıştır. Modelin eğitim doğruluğu %97,83 ve test doğruluğu %83,52 olarak elde edilmiştir. İnternet üzerinden yapılan eğitim ve sunumlarda; dinleyicilerin duygu durumları, geliştirilen CNN modeli ile gerçek zamanlı olarak tespit edilmekte ve tasarlanan algoritma ile eğitim süresince ve eğitimin sonunda katılımcıların duygu yoğunlukları sunucuya zaman bazlı olarak rapor halinde sunulmasını sağlayan bir algoritma geliştirilmiştir. Sunulan rapor sayesinde dinleyicilerin zamana göre duygu durumları analiz edilerek eğitim verimliliği artırılmaktadır.

Anahtar Kelimeler:

Evrişimli Sinir Ağları, Yüz duygu ifadeleri tanımlama, FER2013 veri seti, Eğitim verimliliği

Improving Facial Expression Detection Using Convolutional Neural Networks and Analysis of Education Efficiency

In this paper, a convolutional neural network model (CNN) with higher accuracy than other models in the literature is proposed to detect facial emotional expressions. To train the convolutional neural network model, the FER2013 dataset consisting of 30,000 images and human faces in seven emotion categories was used. The training accuracy of the model was 97.83% and the test accuracy was 83.52%. In training and presentations made over the Internet; The emotional states of the listeners are detected in real time with the developed CNN model, and an algorithm has been developed that allows the emotional intensity of the participants to be reported to the presenter on a time basis during the training and at the end of the training with the designed algorithm. Thanks to the presented report, the emotional states of the listeners are analyzed according to time, thereby increasing the educational efficiency.

Keywords:

Convolutional Neural Networks, Identifying facial expressions of emotions, FER2013 dataset, Online education efficiency,

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