Application of Emotion Analysis in Deep Learning Techniques

Authors : Mesut Uysal, Mehmet Fatih Demiral, Ali Hakan Işik

Pages : 919-935

Doi:10.24012/dumf.1757225

View : 63 | Download : 107

Publication Date : 2025-12-30

Article Type : Research Paper

Abstract :Emotion recognition has become a pivotal technology in advancing human-computer interaction with applications spanning fields such as healthcare, entertainment, and customer experience. This paper evaluates the performance of five deep learning models—YOLOv8m-cls, ResNet50, EfficientNetB5, MobileNetV2, and DenseNet121—in detecting emotions from facial expressions. Leveraging the AffectNet dataset, which initially contained eight emotional categories, we focused on five emotions after excluding three due to low data availability and similarity. The emotions processed include anger, happiness, sadness, surprise, and fear. The models were fine-tuned through transfer learning, demonstrating that YOLOv8m-cls performed best, balancing accuracy, speed, and generalization, making it suitable for real-time applications. ResNet50 and EfficientNetB5 also performed well, with ResNet50 excelling in handling complex facial features and EfficientNetB5 offering computational efficiency with high accuracy. The study also highlights challenges such as intra-class variability and inter-class similarity, which continue to affect model performance. These findings underscore the importance of selecting model architectures based on specific application requirements and suggest that future research should explore integrating multimodal data to enhance emotion recognition systems.
Keywords : Duygu tanıma, Derin öğrenme, Transfer öğrenme, Yüz ifadesi, YOLOv8m-cls

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