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Toward a flexible facial analysis framework in OpenISS for visual effects

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Toward a flexible facial analysis framework in OpenISS for visual effects

Shen, Yiran (2019) Toward a flexible facial analysis framework in OpenISS for visual effects. Masters thesis, Concordia University.

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Abstract

Facial analysis, including tasks such as face detection, facial landmark detection, and facial expression recognition, is a significant research domain in computer vision for visual effects. It can be used in various domains such as facial feature mapping for
movie animation, biometrics/face recognition for security systems, and driver fatigue monitoring for transportation safety assistance. Most applications involve basic face and landmark detection as preliminary analysis approaches before proceeding into further specialized processing applications. As technology develops, there are plenty of implementations and resources for each task available for researchers, but the key missing properties among them all are fexibility and usability. The integration of functionality components involves complex configurations for each connection joint which is typically problematic with poor reusability and adjustability. The lack of support for integrating different functionality components greatly impact the research effort and cost for individual researchers, which also leads us to the idea of providing a framework solution that can help regarding the issue once and for all. To address this
problem, we propose a user-friendly and highly expandable facial analysis framework solution. It contains a core that supports fundamental services for the framework, and a facial analysis module composed of implementations for facial analysis tasks.
We evaluate our framework solution and achieve our goals of instantiating the facial analysis specialized framework, which essentially perform tasks in face detection, facial landmark detection, and facial expression recognition. This framework solution as a whole, solves the industry problem of lacking an execution platform for integrated facial analysis implementations and fills the gap in visual effects industry.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:Shen, Yiran
Institution:Concordia University
Degree Name:M. Comp. Sc.
Program:Computer Science
Date:29 August 2019
Thesis Supervisor(s):Paquet, Joey and Mokhov, Serguei
ID Code:985787
Deposited By: Yiran Shen
Deposited On:06 Feb 2020 02:47
Last Modified:06 Feb 2020 02:47
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