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An Investigation into Modern Facial Expressions Recognition by a Computer

Abstract Facial Expressions Recognition using the Convolution Neural Network has been actively researched upon in the last decade due to its high number of applications in the human-computer interaction domain. As Convolution Neural Networks have the exceptional ability to learn, they outperform the methods using handcrafted features. Though the state-of-the-art models achieve high accuracy on the lab-controlled images, they still struggle for the wild expressions. Wild expressions are captured in a real-world setting and have natural expressions. Wild databases have many challenges such as occlusion, variations in lighting conditions and head poses. In this work, I address these challenges and propose a new model containing a Hybrid Convolutional N... (more)
Created Date 2019
Contributor Chhabra, Sachin (Author) / Li, Baoxin (Advisor) / Venkateswara, Hemanth (Committee member) / Srivastava, Siddharth (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computer Vision / Deep Learning / Facial Expression Recognition / RAF-DB
Type Masters Thesis
Extent 64 pages
Language English
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis