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Protected multimodal emotion recognition

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thesis
posted on 24.05.2021, 18:26 by Kevin Tang
In this thesis, we propose Protected Multimodal Emotion recognition (PMM-ER), an emotion recognition approach that includes security features against the growing rate of cyber-attacks on various databases, including emotion databases. The analysis on the frequently used encryption algorithms has led to the modified encryption algorithm proposed in this work. The system is able to recognize 7 different emotions, i.e. happiness, sadness, surprise, fear, disgust and anger, as well as a neutral emotion state, based on 2D video frames, 3D vertices, and audio wave information. Several well-known features are employed, including the HSV colour feature, iterative closest point (ICP) and Mel-frequency cepstral coefficients (MFCCs). We also propose a novel approach to feature fusion including both decision- and feature-level fusion, and some well-known classification and feature extraction algorithms such as principle component analysis (PCA), linear discernment analysis (LDA) and canonical correlation analysis (CCA) are compared in this study.

History

Degree

Master of Applied Science

Program

Electrical and Computer Engineering

Granting Institution

Ryerson University

LAC Thesis Type

Thesis