Patient Facial Emotion Recognition and Sentiment Analysis Using Secure Cloud with Hardware Acceleration

dc.contributor.advisorDuan, Lide
dc.contributor.authorTorres, Alexander D.
dc.contributor.committeeMemberLin, Wei-Ming
dc.contributor.committeeMemberLee, Wonjun
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractCurrently, cloud computing has become a de-facto platform for data management and analytics. Meanwhile, data stream processing from smart devices leads to new opportunities and challenges with regards to reliable real-time data analysis. Distributed image understanding across heterogeneous computing platforms, such as smart devices and cloud analytic platforms, poses a great motivation to advance conventional data analytics algorithms to distributed heterogynous mobile cloud computing. The facial emotion recognition system presented in this article has a decoupled architecture where video processing, face detection, and facial emotion recognition inference tasks are run on a local client machine while the intelligence models used for these tasks reside in a cloud-based remote storage. Training of the intelligence models used for these tasks is also conducted in a cloud environment using GPU's. This decoupled design transforms remote GPU resources into a utility-based cloud service, providing cloud-based hardware acceleration to a wide range of distributed facial emotion recognition tasks.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent45 pages
dc.subjectcloud computing
dc.subjectdeep learning
dc.subjectemotion recognition
dc.subjectmachine learning
dc.subjectneural network
dc.subjectsentiment analysis
dc.subject.classificationComputer engineering
dc.subject.classificationComputer science
dc.titlePatient Facial Emotion Recognition and Sentiment Analysis Using Secure Cloud with Hardware Acceleration
dcterms.accessRightspq_closed and Computer Engineering of Texas at San Antonio of Science


Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
4.3 MB
Adobe Portable Document Format