The effects of sensor and feature level fusion methods in multimodal emotion analysis

Year 2024, , 1093 - 1099, 15.10.2024

Bahar Hatipoğlu Yılmqz , Cemal Köse

https://doi.org/10.28948/ngumuh.1469289

Abstract

Fusion-based studies on multimodal emotion recognition (MER) are very popular nowadays. In this study, EEG signals and facial images are fused using Sensor Level Fusion (SLF) and Feature Level Fusion (FLF) methods for multimodal emotion recognition. The general procedure of the study is as follows. First, the EEG signals are converted into angle amplitude graph (AAG) images. Second, the most unique ones are automatically identified from all face images obtained from video recordings. Then, these modalities are fused separately using SLF and FLF methods. The fusion approaches were used to combine the obtained data and perform classification on the integrated data. The experiments were performed on the publicly available DEAP dataset. The highest accuracy was 82.14% with 5.26 standard deviations for SLF and 87.62% with 6.74 standard deviations for FLF. These results show that this study makes an important contribution to the field of emotion recognition by providing an effective method.

Keywords

Multimodal emotion recognition, Sensor level fusion, Feature level fusion, DEAP dataset

Project Number

121E002

Çok modlu duygu analizinde sensör ve özellik seviyesi füzyon yöntemlerinin etkileri

Abstract

Füzyon tabanlı çok modlu duygu tanıma (MER) çalışmaları günümüzde oldukça popülerdir. Bu çalışmada, çok modlu duygu tanıma için EEG sinyalleri ve yüz görüntüleri sensör seviyesinde füzyon (SLF) ve öznitelik seviyesinde füzyon (FLF) yöntemleri ile birleştirilmiştir. Çalışmanın genel akışı şu şekildedir. İlk olarak EEG sinyalleri açı genlik grafiği (AAG) görüntülerine dönüştürülmektedir. İkinci olarak, video kayıtlarından elde edilen tüm yüz görüntülerinden en benzersiz olanlar otomatik olarak belirlenmektedir. Daha sonra, bu modaliteler SLF ve FLF yöntemleri kullanılarak ayrı ayrı birleştirilmektedir. Elde edilen verileri birleştirmek ve bütünleşik veriler üzerinde sınıflandırma yapmak için füzyon yaklaşımlar kullanılmıştır. Deneyler halka açık DEAP veri kümesi üzerinde gerçekleştirilmiştir. En yüksek doğruluk SLF için 5,26 standart sapma ile %82,14 ve FLF için 6,74 standart sapma ile %87,62 olarak elde edilmiştir. Bu sonuçlar, bu çalışmanın etkili bir yöntem sunarak duygu tanıma alanına önemli bir katkı sağladığını göstermektedir.

Keywords

Çok modlu duygu tanıma, Sensör seviyesinde füzyon, Öznitelik seviyesinde füzyon, DEAP veri seti

Project Number

121E002

Thanks

This research was supported by the Turkish Scientific and Research Council (TUBITAK) through project 121E002 and 119E397.

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