Derin Öğrenme Tabanlı Gerçek Zamanlı Vücut Hareketlerinden Duygu Analizi Modeli

Year 2024, Volume: 12 Issue: 2, 664 - 674, 29.06.2024

Mustafa Yağcı , Muhammed Erkam Aygül

https://doi.org/10.29109/gujsc.1384031

Abstract

İletişim, canlılar arasında bilgi, duygu ve düşüncelerin aktarılması için kullanılan bir araçtır. Araştırmalar, insan iletişiminin yaklaşık %80'inin beden dili veya mimiklerle yapıldığını göstermektedir. Bir kişinin beden dilini yorumlamak ise o kişinin duygularından çıkarım yapılmasını sağlayabilir. Bu çalışmanın amacı, gerçek zamanlı olarak insan vücut hareketlerini derin öğrenme teknikleri ile analiz ederek bireyin duygusal durumlarını tahmin eden yeni bir makine öğrenmesi modeli geliştirmektir. İnsanların vücudunu tanıyan ve tanınan vücut pozuna iskelet modelin oturtulması işlemi ile o pozdan veri toplayan model geliştirilmiş, toplanan veri seti ile farklı modeller eğitilmiştir. Bununla birlikte gerçek zamanlı analiz yapılması için bir tahmin modeli geliştirilmiştir. Bu sayede insanların beden dili ve mimiklerinden duygu çıkarımı yapılabilmektedir. Yapılan farklı test modellerinin sonuçlarının doğruluk oranının en düşük %92.86, en yüksek %97.80 olduğu ve gerçek zamanlı analiz sonuçlarının doğruluk oranının %94.50 olduğu görülmüştür. Bu çalışmanın sonuçları daha iyi insan-makine etkileşimi, duygusal zekâ uygulamaları ve sosyal etkileşim alanlarında kullanılabilir.

Keywords

Görüntü işleme, makine öğrenmesi, yapay zekâ, duygu analizi, nitelik çıkarımı

Deep Learning Based Emotion Analysis Model from Real-Time Body Movements

Abstract

Communication is a tool used to transfer information, feelings and thoughts between living things. Research shows that approximately 80% of human communication is done through body language or gestures. Interpreting a person's body language can enable inferences from that person's emotions. The aim of this study is to develop a new machine learning model that predicts an individual's emotional states by analyzing human body movements in real time with deep learning techniques. A model that recognizes people's bodies and collects data from that pose by fitting a skeleton model to the recognized body pose has been developed, and different models have been trained with the collected data set. In addition, a prediction model has been developed for real-time analysis. In this way, emotions can be inferred from people's body language and facial expressions. It was observed that the accuracy rate of the results of the different test models was 92.86% at the lowest, 97.80% at the highest, and the accuracy rate of the real-time analysis results was 94.50%. The results of this study can be used in the fields of better human-machine interaction, emotional intelligence applications and social interaction.

Keywords

Image processing, machine learning, artificial intelligence, emotion analysis, feature extraction.

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