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Facial Expression Detection using PCA, a Thermal Approach. A brief foray into thermographic and visual imaging approaches. Robert S. Pienta. Table of Contents. Background Methodology Data Collection Data Analysis Results Conclusion & Future Work. Background.
A brief foray into thermographic and visual imaging approaches.
Robert S. Pienta
To capture individual action units a set of regions of interest (ROIs) were selected.
These ROIs are placed at very particular locations on the face.
Each ROI tracks the contraction of a muscle by detecting the tissue deformation via changes in the heat map.
For each ROI:
Frame: 1 … k, where k is the final frame.
Each frame of the video is added to a matrix by vectorizing it.
The coefficients of the first principle components, denoting change in a ROI over time, as expressions are formed. This denotes change in just one ROI over the total number of frames.
We then calculate the standard deviation of the PCA output for the course of an expression.
As we have 13 different regions, we create an expression profile for that expression.
Place 13 Fundamental ROIs
Slice every frame for each ROI into a vector
ROI Frame Sequences
Standard deviation of PCA output for each expression
Combine the vector representation of each frame into a matrix
Apply PCA to each ROI matrix
Machine learning based classification
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