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given set of facesthe object is face recognition. we project the faces to new fielad of eigen faces which are actualy eigen vectors the same as PCA algorithm
THANKS TO THE SITE http://fewtutorials.bravesites.com/tutorials
steps
1) resize all M faces to N*N
2) remove average
3) create matrix A of faces each row N*N
totla size of A is (N*N) * M
4) calculate average face
5) remove average face from A
6) compute the covariance matrix C A'*A , C size is M*M
7) compute eigen values and eigen vectors , to compute the eigne faces need to go bacj to higher dimension
8) compute the linear combination of each original face
9( given new face project it to eigen face and compute distance to each eigen face this is the recognition.
Cita come
michael scheinfeild (2026). eigenfaces algorithm (https://it.mathworks.com/matlabcentral/fileexchange/45915-eigenfaces-algorithm), MATLAB Central File Exchange. Recuperato .
Categorie
Scopri di più su Dimensionality Reduction and Feature Extraction in Help Center e MATLAB Answers
Informazioni generali
- Versione 1.0.0.0 (5,16 KB)
Compatibilità della release di MATLAB
- Compatibile con qualsiasi release
Compatibilità della piattaforma
- Windows
- macOS
- Linux
| Versione | Pubblicato | Note della release | Action |
|---|---|---|---|
| 1.0.0.0 |
