eigenfaces algorithm
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.
Citar como
michael scheinfeild (2024). eigenfaces algorithm (https://www.mathworks.com/matlabcentral/fileexchange/45915-eigenfaces-algorithm), MATLAB Central File Exchange. Recuperado .
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformas
Windows macOS LinuxCategorías
- AI and Statistics > Statistics and Machine Learning Toolbox > Dimensionality Reduction and Feature Extraction >
- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation > Object Detection Using Features > Face Detection >
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