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dc.contributorKing, Scott A.
dc.contributorSheta, Alaa
dc.contributorRahnemoonfar, Maryam
dc.creatorHu, Jiaqi
dc.date2017-11-02T19:49:32Z
dc.date2017-11-02T19:49:32Z
dc.date2017-08
dc.date.accessioned2018-01-22T22:24:06Z
dc.date.available2018-01-22T22:24:06Z
dc.identifierhttp://hdl.handle.net/1969.6/5630
dc.identifier.urihttp://hdl.handle.net/1969.6/5630
dc.descriptionA thesis Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in COMPUTER SCIENCE from Texas A&M University-Corpus Christi in Corpus Christi, Texas.
dc.descriptionFacial expression recognition has been a challenge for many years. With the recent growth in machine learning, a real-time facial expression recognition system using deep learning technology can be useful for an emotion monitoring system for Human-computer interaction(HCI). We proposed a Personal Facial Expression Monitoring System (PFEMS). We designed a custom Convolutional Neural Network model and used it to train and test different facial expression images with the TensorFlow machine learning library. PFEMS has two parts, a recognizer for validation and a data training model for data training. The recognizer contains a facial detector and a facial expression recognizer. The facial detector extracts facial images from video frames and the facial expression recognizer distinguishes the extracted images. The data training model uses the Convolutional Neural Network to train data and the recognizer also uses Convolutional Neural Network to monitor the emotional state of a user through their facial expressions. The system recognizes the six universal emotions, angry, disgust, happy, surprise, sad and fear, along with neutral.
dc.descriptionComputing Sciences
dc.descriptionCollege of Science and Engineering
dc.format53 pages.
dc.languageen_US
dc.rightsThis material is made available for use in research, teaching, and private study, pursuant to U.S. Copyright law. The user assumes full responsibility for any use of the materials, including but not limited to, infringement of copyright and publication rights of reproduced materials. Any materials used should be fully credited with its source. All rights are reserved and retained regardless of current or future development or laws that may apply to fair use standards. Permission for publication of this material, in part or in full, must be secured with the author and/or publisher.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/us/
dc.rightsHu, Jiaqi
dc.subjectCNN
dc.subjectFacial Expression Recognition
dc.titleA personal facial expression monitoring system using deep learning
dc.typeText
dc.typeThesis


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