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




Torres, Alexander D.

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Currently, 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.


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cloud computing, deep learning, emotion recognition, machine learning, neural network, sentiment analysis



Electrical and Computer Engineering