EMOTION CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS WITH DIFFERENT ARCHITECTURES
ӘРТҮРЛІ АРХИТЕКТУРАЛАРЫ БАР КОНВОЛЮЦИЯЛЫҚ НЕЙРОНДЫҚ ЖЕЛІЛЕРДІ ҚОЛДАНУ АРҚЫЛЫ ЭМОЦИЯЛАРДЫ КЛАССИФИКАЦИЯЛАУ
КЛАССИФИКАЦИЯ ЭМОЦИЙ С ИСПОЛЬЗОВАНИЕМ СВЕРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ С РАЗЛИЧНЫМИ АРХИТЕКТУРАМИ
Yershov E. Orynbassar S. Zholamanov B. Nurgaliyev M. Dosymbetova G. Khumarbekkyzy T.
2025Kazakh-British Technical University
Herald of the Kazakh British Technical UNiversity
2025#22Issue 2110 - 126 pp.
Thermal imaging offers a non-invasive and robust approach to emotion recognition by capturing facial temperature patterns that correlate with psychophysiological states. This study investigates the application of deep neural networks to classify six basic human emotions – happiness, sadness, fear, disgust, anger, and surprise – using facial thermograms. A balanced dataset was collected under controlled experimental conditions, and four de ep learning architectures were evaluated: Convolutional Neural Network (CNN), Fully Convolutional Network (FCN), EfficientNet, and MobileNet. The models were trained and tested on a curated set of preprocessed thermal facial images. Among the evaluated architectures, FCN achieved the highest classification accuracy of 90.04%. The results demonstrate that deep learning models, particularly FCNs, are well-suited for emotion recognition from thermal data, with potential applications in psychophysiological monitoring, healthcare, and real-time human-computer interaction systems.
CNN , Efficient Net , Fully Convolution Network , Mobile Net , neural networks , thermograms
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Al-Farabi Kazakh National University, Almaty, Kazakhstan
Al-Farabi Kazakh National University
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026