Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4201
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dc.contributor.authorGupta, Sakshi-
dc.contributor.authorSengupta, Anwesha-
dc.date.accessioned2023-12-28T11:27:28Z-
dc.date.available2023-12-28T11:27:28Z-
dc.date.issued2023-12-
dc.identifier.citation3rd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET- 2023), NIT Patna, 21-22 December 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4201-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractFacial emotion recognition involves the analysis of facial expressions to identify and classify human emotions. As there is a relationship between facial temperature changes and emotions, this study focuses on facial emotion recognition using thermal images. This work aims to improve the efficiency, robustness, and accuracy of emotion recognition systems to improve human-computer interaction, mental health monitoring, personalized education, marketing effectiveness, and security. The proposed Convolution Neural Network (CNN) architecture is designed to recognize human emotions by classifying facial images into six distinct categories (happiness, sadness, anger, fear, disgust, and surprise). With an average detection accuracy of 0.9857, the proposed model showcases its effectiveness in emotion prediction. The findings indicate that our proposed system outperforms YOLOv5 and YOLOv5-NMS models across all emotions, showcasing superior results.en_US
dc.subjectemotion recognitionen_US
dc.subjectthermal imagesen_US
dc.subjectdeep learningen_US
dc.subjectfacial expressionen_US
dc.titleUnlocking Emotions Through Heat: Facial Emotion Recognition via Thermal Imagingen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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