Abstract :

Emotion Recognition Is A Critical Research Area In Human–computer Interaction, Driving Applications In Healthcare, Surveillance, And Intelligent Systems. To Overcome The Limitations Of Unimodal Systems, This Work Introduces A Multimodal Emotion Recognition Framework That Integrates Convolutional Neural Networks (CNNs) And Long Short-Term Memory (LSTM) Networks To Capture Both Spatial And Temporal Features. Specifically, CNNs Extract High-level Spatial Representations From Visual Inputs Like Facial Expressions, While LSTMs Model The Temporal Dependencies Found In Sequential Data Such As Speech Signals And Facial Motion Dynamics. By Fusing Information Across Multiple Modalities For Complementary Feature Learning, The Architecture Achieves Superior Robustness And Recognition Accuracy. Experimental Evaluations On Benchmark Datasets Show That This CNN–LSTM Model Outperforms Traditional Machine Learning Methods And Standalone Deep Learning Approaches, Confirming Its Effectiveness For Real-world Deployment.

Published:

24-9-2026

Issue:

Vol. 26 No. 9 (2026)

Page Nos:

360 - 366

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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