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Hybrid CNN–Random Forest Architecture with Residual Connections and Attention for Real-Time Facial Emotion Recognition


Authors : P. Penchala Prasad; Y. Naga Purushotam; T. Raju; V. Lokesh Yadav

Volume/Issue : Volume 11 - 2026, Issue 5 - May


Google Scholar : https://tinyurl.com/442hp2hd

DOI : https://doi.org/10.38124/ijisrt/26may189

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The problem of facial emotion recognition (FER) is one of the primary subjects studied by the community of affective computing. Its possible applications include many disciplines such as human-computer interaction, diagnosis of mental disorders, and surveillance. Within this work, a new model for emotion recognition using convolutional neural networks with residual connections, channel attention mechanism, and random forest classifier for recognition of seven emotion classes is suggested for use in the context of FER tasks.

Keywords : Facial Emotion Recognition, Convolutional Neural Network, Random Forest, Residual Connections, Channel Attention, CLAHE, FER-2013, Affective Computing, Hybrid Classifier.

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The problem of facial emotion recognition (FER) is one of the primary subjects studied by the community of affective computing. Its possible applications include many disciplines such as human-computer interaction, diagnosis of mental disorders, and surveillance. Within this work, a new model for emotion recognition using convolutional neural networks with residual connections, channel attention mechanism, and random forest classifier for recognition of seven emotion classes is suggested for use in the context of FER tasks.

Keywords : Facial Emotion Recognition, Convolutional Neural Network, Random Forest, Residual Connections, Channel Attention, CLAHE, FER-2013, Affective Computing, Hybrid Classifier.

Paper Submission Last Date
31 - October - 2026

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