Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model

Abdoul Malik; Mohammad Adnan Ayoubi; Mohammad Alsuedani

EN TR

Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model

Abstract

Facial emotion recognition is a sophisticated approach that uses facial expression analysis to identify and understand human emotions. Its numerous applications in human-computer interaction, healthcare, and market research have recently received much attention. This technology aims to develop sophisticated algorithms and systems capable of accurately identifying and understanding an individual’s emotional state by analysing facial features. This study focuses on classifying human emotions using a deep learning model based on the ResNet50v2 architecture. This work presents a comprehensive facial expression recognition model utilising the FER-2013 dataset, which includes thousands of images annotated with seven distinct emotions: happy, angry, neutral, sad, disgust, fear, and surprise. Our approach involves several key steps, including significant image preprocessing to improve the input data quality, image transformation to increase the diversity and robustness of the model, and implementing a modified ResNet50v2 architecture to improve recognition accuracy. Our model achieved a 69% accuracy on the test data, demonstrating competitive performance compared to existing models applied to FER-2013. The results of this study highlight the great potential of deep learning methods in precisely identifying and deciphering human emotions from facial expressions, paving the way for more emotionally intelligent and responsive human-computer interaction systems.

Keywords

Facial emotion recognition, Emotion classification, FER-2013, ResNet50v2

ResNet50v2 Modelini Kullanarak FER-2013 Veri Setinde Yüz Duygularını Tanımak için Derin Öğrenme

Abstract

Yüz duygu tanıma, yüz ifadesi analizini kullanarak insan duygularını tanımlamak ve anlamak için kullanılan sofistike bir yaklaşımdır. İnsan-bilgisayar etkileşimi, sağlık hizmetleri ve pazar araştırması alanlarında sayısız uygulaması son zamanlarda büyük ilgi görmüştür. Bu teknoloji, yüz özelliklerini analiz ederek bir bireyin duygusal durumunu doğru bir şekilde tanımlayıp anlayabilen sofistike algoritmalar ve sistemler geliştirmeyi amaçlamaktadır. Bu çalışma, ResNet50v2 mimarisine dayalı bir derin öğrenme modeli kullanarak insan duygularını sınıflandırmaya odaklanmaktadır. Bu çalışma, mutlu, kızgın, nötr, üzgün, tiksinti, korku ve şaşkınlık olmak üzere yedi farklı duygu ile etiketlenmiş binlerce görüntüyü içeren FER-2013 veri setini kullanan kapsamlı bir yüz ifadesi tanıma modeli sunmaktadır. Yaklaşımımız, girdi veri kalitesini iyileştirmek için önemli görüntü ön işleme, modelin çeşitliliğini ve sağlamlığını artırmak için görüntü dönüştürme ve tanıma doğruluğunu iyileştirmek için değiştirilmiş bir ResNet50v2 mimarisi uygulamak gibi birkaç önemli adımı içermektedir. Modelimiz, test verilerinde %69 doğruluk oranına ulaşarak, FER-2013'e uygulanan mevcut modellere kıyasla rekabetçi bir performans sergilemiştir. Bu çalışmanın sonuçları, yüz ifadelerinden insan duygularını hassas bir şekilde tanımlama ve deşifre etme konusunda derin öğrenme yöntemlerinin büyük potansiyelini vurgulamakta ve daha duygusal zeka ve duyarlılığa sahip insan-bilgisayar etkileşim sistemlerinin önünü açmaktadır.

Keywords

Yüz duygu tanıma, Duygu sınıflandırma, FER-2013, ResNet50v2

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Abdoul Malik
0009-0006-6525-8346
Türkiye

Mohammad Adnan Ayoubi This is me
0009-0003-5647-5558
Türkiye

Mohammad Alsuedani This is me
0009-0002-4528-971X
Türkiye

Publication Date

November 13, 2025

Submission Date

January 6, 2025

Acceptance Date

September 29, 2025

Published in Issue

Year 2025 Volume: 5 Number: 2

IZ

https://izlik.org/JA75TF96AP

APA

Malik, A., Ayoubi, M. A., & Alsuedani, M. (2025). Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model. OMÜ Mühendislik Bilimleri Ve Teknolojisi Dergisi, 5(2), 19-33. https://izlik.org/JA75TF96AP

AMA

1.Malik A, Ayoubi MA, Alsuedani M. Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model. OMUJEST. 2025;5(2):19-33. https://izlik.org/JA75TF96AP

Chicago

Malik, Abdoul, Mohammad Adnan Ayoubi, and Mohammad Alsuedani. 2025. “Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset Using the ResNet50v2 Model”. OMÜ Mühendislik Bilimleri Ve Teknolojisi Dergisi 5 (2): 19-33. https://izlik.org/JA75TF96AP.

EndNote

Malik A, Ayoubi MA, Alsuedani M (November 1, 2025) Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model. OMÜ Mühendislik Bilimleri ve Teknolojisi Dergisi 5 2 19–33.

IEEE

[1]A. Malik, M. A. Ayoubi, and M. Alsuedani, “Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model”, OMUJEST, vol. 5, no. 2, pp. 19–33, Nov. 2025, [Online]. Available: https://izlik.org/JA75TF96AP

ISNAD

Malik, Abdoul - Ayoubi, Mohammad Adnan - Alsuedani, Mohammad. “Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset Using the ResNet50v2 Model”. OMÜ Mühendislik Bilimleri ve Teknolojisi Dergisi 5/2 (November 1, 2025): 19-33. https://izlik.org/JA75TF96AP.

JAMA

1.Malik A, Ayoubi MA, Alsuedani M. Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model. OMUJEST. 2025;5:19–33.

MLA

Malik, Abdoul, et al. “Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset Using the ResNet50v2 Model”. OMÜ Mühendislik Bilimleri Ve Teknolojisi Dergisi, vol. 5, no. 2, Nov. 2025, pp. 19-33, https://izlik.org/JA75TF96AP.

Vancouver

1.Abdoul Malik, Mohammad Adnan Ayoubi, Mohammad Alsuedani. Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model. OMUJEST [Internet]. 2025 Nov. 1;5(2):19-33. Available from: https://izlik.org/JA75TF96AP

Deep Learning for Facial Emotion Recognition on the FER-2013 Dataset using the ResNet50v2 Model

Abstract

Keywords

ResNet50v2 Modelini Kullanarak FER-2013 Veri Setinde Yüz Duygularını Tanımak için Derin Öğrenme

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

IZ

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