2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 375-385, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Santosh B. Dhekale 1 * , S. S. Nikam 2 , D. K. Shedge 3
Doi: https://doi.org/10.54216/JISIoT.180226
Human-computer interaction (HCI), artificial intelligence (AI), and HI are in high demand these days. In fields like marketing, client feedback analysis, security, and healthcare, facial expression- grounded emotion recognition becomes a pivotal tool for comprehending mortal feelings. Facial expressions like fear, disgust, surprise, anger, sadness, and happiness are pivotal pointers of emotional countries. Businesses can ameliorate client gests by relating these pointers and measuring client satisfaction with goods or services. The discovery of mortal feelings has been achieved with machine literacy algorithms like support vector machines and arbitrary timbers. The effectiveness of deep literacy models for emotion discovery has been validated by earlier studies that employed Convolutional Neural Networks (CNNs) to reliably classify feelings grounded on facial expressions. Likewise, recent developments in deep literacy, particularly the operation of Convolutional Neural Networks (CNNs), have significantly increased the delicacy of facial emotion recognition and interpretation from images and live camera aqueducts. In order to reuse face images with CNN models for real- time emotion recognition, our exploration attempts to produce an emotion recognition system using Python and OpenCV. The current study describes how to watch live videotape aqueducts for facial expressions to identify which of the seven linked feelings is most likely to do. This system provides emotional behavior in real time when needed.
Convolutional Neural Network (CNN) , Emotion Recognition , OpenCV , Machine Learning , Sentiment Analysis , Real-Time Monitoring
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