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Mehdi Ghayoumi - Dissertationp.pdf (1.88 MB)

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FACIAL EXPRESSION ANALYSIS USING DEEP LEARNING WITH PARTIAL INTEGRATION TO OTHER MODALITIES TO DETECT EMOTION

Ghayoumi, Mehdi
http://orcid.org/0000-0002-5771-9644
http://rave.ohiolink.edu/etdc/view?acc_num=kent1501273062260458

Abstract Details

2017, PHD, Kent State University, College of Arts and Sciences / Department of Computer Science.
Analysis of human emotion is very important as the field of social robotics where a new generation of humanoids and other smart devices will interact with humans. Emotional expression is a universal language for interaction with humans. Understanding human emotions is a necessary and important step for human-computer interaction. Human emotion is expressed as a complex combination of facial expressions, speech (including silence) and gestures postures, various limb-motions, gaze, and blinking. Multiple research models have been developed for limited facial expression analysis, speech based emotion analysis, limited models for gesture analysis and their limited integration. However, such analysis is limited to single frame analysis time-efficiency, limited handling of occlusion, notion of colors in facial expression analysis, lack of exploitation of symmetry, lack of dynamic change in assigning weight between the modalities based upon environmental requirement and six basic emotions. This research develops a convolutional neural network based deep learning model that recognizes human facial expressions exploiting a combination of symmetrical representation to handle occlusion; a unified model based upon transforming facial muscle motion to geometric feature points; fusion of multiple modalities and fast hashing techniques for real-time emotion recognition. It also proposes a new model for recognition of mixed-emotion in real-time.
Arvind K. Bansal (Advisor)
Javed I. Khan (Committee Member)
Cheng Chang Lu (Committee Member)
Stephen B. Fountain (Committee Member)
William E. Merriman (Committee Member)
149 p.
Artificial Intelligence; Computer Science; Robotics; Robots
Human Computer Interaction, Emotion, Facial Expression, Deep Learning, Convolutional Neural Networks, Social Robots

Recommended Citations

Citations

  • Ghayoumi, M. (2017). FACIAL EXPRESSION ANALYSIS USING DEEP LEARNING WITH PARTIAL INTEGRATION TO OTHER MODALITIES TO DETECT EMOTION [Doctoral dissertation, Kent State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=kent1501273062260458

    APA Style (7th edition)

  • Ghayoumi, Mehdi. FACIAL EXPRESSION ANALYSIS USING DEEP LEARNING WITH PARTIAL INTEGRATION TO OTHER MODALITIES TO DETECT EMOTION. 2017. Kent State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=kent1501273062260458.

    MLA Style (8th edition)

  • Ghayoumi, Mehdi. "FACIAL EXPRESSION ANALYSIS USING DEEP LEARNING WITH PARTIAL INTEGRATION TO OTHER MODALITIES TO DETECT EMOTION." Doctoral dissertation, Kent State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=kent1501273062260458

    Chicago Manual of Style (17th edition)

kent1501273062260458
340
© 2017, all rights reserved.
This open access ETD is published by Kent State University and OhioLINK.