Facial Emotion Recognition With VGG-11 Based On Classification Algorithms

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 359 - 365, 20.10.2021

Kenan Donuk , Davut Hanbay

https://doi.org/10.53070/bbd.990613

Cited By: 2

Abstract

Facial emotional expressions are a non-verbal communication tool in people's communication with each other. These expressions give information about people's thoughts. In the light of this information, studies are carried out in many areas such as customer satisfaction, detection of mental disorders, autism, detection of lies and fear. There are many traditional and deep learning-based methods for emotion recognition task. In the study, FER2013 dataset and VGG-11, one of the deep learning-based architectures, were used for emotion recognition. Test accuracy of 68.32% was achieved with the VGG-11 architecture. In this article, the effect of classification methods applied to the feature layer of the VGG-11 architecture on emotion recognition accuracy is examined.

Keywords

Vgg-11, facial emotion recognition, fer2013

Project Number

FDK-2020-2110

Sınıflandırma Algoritmalarına Dayalı VGG-11 ile Yüzde Duygu Tanıma

Abstract

Yüz duygu ifadeleri insanların birbirleriyle olan iletişiminde sözlü olmayan bir iletişim aracıdır. Bu ifadeler insanların düşünceleri hakkında bilgiler vermektedir. Bu bilgiler ışığında müşteri memnuniyeti, zihinsel bozuklukların tespiti, otizm, yalan ve korku tespiti gibi birçok alanda çalışmalar yapılmaktadır. Duygu tanıma görevi için yapılmış geleneksel ve derin öğrenme tabanlı birçok yöntem mevcuttur. Yapılan çalışmada duygu tanıma için FER2013 veriseti ve derin öğrenme tabanlı mimarilerden VGG-11 kullanılmıştır. VGG-11 mimarisi ile %68.32’lik test doğruluğu elde edilmiştir. Çalışmada VGG-11 mimarisinin özellik katmanına uygulanan sınıflandırma yöntemlerinin duygu tanıma doğruluğuna etkisi incelenmiştir.

Keywords

Vgg-11, yüz duygu tanıma, fer2013

Supporting Institution

İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP)

Project Number

FDK-2020-2110

Thanks

Bu çalışma İnönü Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi (BAP) tarafından FDK-2020-2110 kodlu proje ile desteklenmiştir.

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