aspg-18-4080-1792025504
20261015005144
American Scientific Publishing Group
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American Scientific Publishing Group
Journal of Intelligent Systems and Internet of Things
JISIoT
2769-786X
2690-6791
2026
18
2
Emotion Recognition Using Deep Learning via Facial Expression
Santosh B.
Dhekale
Department of E & TC Engineering, AISSMS Institute of Information Technology, Pune, India; Department of E & TC Engineering, AISSMS College of Engineering, Pune, India
S. S.
Nikam
Department of E & TC Engineering, AISSMS College of Engineering, Pune, India
D. K.
Shedge
Department of E & TC Engineering, AISSMS Institute of Information Technology, Pune, India
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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.
2026
375
385
4080
https://creativecommons.org/licenses/by/4.0/
10.54216/JISIoT.180226
https://americaspg.com/journal/18/article/4080
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https://americaspg.com/storage/91757259103.pdf