Real Time Facial Expression Recognition using EfficientNetB7 Model Architecture

dc.contributor.advisorKshirsagar, Vivek
dc.contributor.authorGattu, Anwesh
dc.date.accessioned2024-04-04T13:36:37Z
dc.date.available2024-04-04T13:36:37Z
dc.date.issued2024
dc.description.abstractFacial expression recognition (FER) represents a pivotal aspect of human interaction and emotional communication. This research delves into the development and implementation of a real-time facial expression recognition system using the FER2013 dataset obtained from Kaggle. Leveraging the cutting-edge EfficientNetB7 model, the study aimed to create a robust and accurate model capable of detecting various facial expressions in real-time through webcam feeds. Employing a combination of Machine Learning and Deep Learning methodologies, the project focused on training the model to categorize facial expressions into distinct emotions. The methodologies encompassed preprocessing steps, data augmentation techniques, and the design of an EfficientNet7 architecture tailored to the nuances of facial feature recognition. The model's performance was evaluated using metrics such as accuracy, precision, recall, and loss. This research encapsulates the comprehensive approach and findings achieved in the pursuit of advancing real-time facial expression recognition technology.
dc.identifier.citationGattu, A. (2024) Real Time Facial Expression Recognition using EfficientNetB7 Model Architecture. Master's Thesis, Dublin Business School.
dc.identifier.urihttps://hdl.handle.net/10788/4527
dc.language.isoen
dc.publisherDublin Business School
dc.rights.holderCopyright, the Author
dc.rights.urihttp://www.esource.dbs.ie/copyright
dc.subjectMachine learning
dc.subjectHuman face recognition (Computer science)
dc.subjectWeb cameras
dc.titleReal Time Facial Expression Recognition using EfficientNetB7 Model Architecture
dc.typeThesis
dc.type.degreelevelMSc
dc.type.degreenameMSc in Data Analytics
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
msc_gattu_a_2024.pdf.pdf
Size:
1.42 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.61 KB
Format:
Item-specific license agreed upon to submission
Description: