Implementation of Facial Expression Recognition

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Resource Overview

This implementation utilizes an image database where the detection phase begins with grayscale conversion, followed by lighting compensation and noise reduction. Edge detection is then performed, and images are normalized to ensure consistency. For feature extraction, PCA (Principal Component Analysis) is employed to extract facial features and project them into vector space. Finally, the system calculates the nearest distance between test images and reference models to determine the most matching expression category.

Detailed Documentation

The system processes images from a database through multiple stages using computer vision techniques. Initially, RGB images are converted to grayscale to reduce computational complexity. Lighting compensation algorithms adjust illumination variations, while noise reduction filters (such as Gaussian or median filters) remove artifacts. Edge detection operators (like Sobel or Canny) highlight facial contours. Normalization ensures uniform image dimensions and intensity ranges. For feature extraction, PCA algorithm identifies dominant facial features by computing eigenvectors from the covariance matrix of training images, projecting them into a lower-dimensional subspace. The recognition phase involves measuring Euclidean distances between test image projections and pre-trained expression models in the feature space, selecting the closest match as the classified expression.

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