Linear Digressions Facial Recognition with Eigenfaces
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Jan 7, 2015 Facial recognition in machine learning is a challenging task due to the high dimensionality of pictures. Principal Component Analysis (PCA) helps reduce dimensions and identify important patterns. Eigenfaces, obtained through PCA, are composite features that represent reconstructions of faces. They play a crucial role in successful facial recognition algorithms, despite their ghostly appearance and challenges in interpretation.
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Facebook Auto-Tagging Example
- Facebook auto-tagging illustrates face recognition in practice.
- Upload photos and the system suggests friends like Katie Malone by matching facial patterns.
Pixels Make Faces High-Dimensional
- Face images are extremely high-dimensional because each pixel is a feature.
- High feature counts lead to overfitting and make computation impractical for many ML algorithms.
The PCA Trade-Off
- Dimensionality reduction balances removing noise and preserving important signal.
- Choose enough principal components to capture meaningful variation without reintroducing overfitting.