FaceRecognitionusingSingularValueDecomposition
As we know about Linear Algebra and its importance in various applications and areas. eigen values and eigen vectors were used here to recognize a given image. Having numerous images as database, firstly we create a normalized image matrix having images stored as column vectors. Now, having computed the image matrix, we calculate its svd components. So, initially we have mxn sized image matrix, after finding svd of it we get U(mxm), sigma(mxn), V(nxn). Here, U forms basis of our database images.
Then we take any random image from the database as a test image, normalize it and project it on the space of U matrix. After projecting it to that space, we calculate euclidean distance of the test image projected on basis space and original column vectors as images. You'll have n values calculated as euclidean distance, one having minimum amount of distance will be your recognized image.
Note: Add some images of faces to a folder having name "Database" in the working directory before running this code.
P.S.: You can also make a bit change and put a threshold, and say all the values of euclidean distance having lesser value than threshold are recognized images.
Citar como
Ronak Prajapati (2024). FaceRecognitionusingSingularValueDecomposition (https://github.com/ronak0001/FaceRecognitionusingSingularValueDecomposition), GitHub. Recuperado .
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformas
Windows macOS LinuxCategorías
- Image Processing and Computer Vision > Computer Vision Toolbox > Recognition, Object Detection, and Semantic Segmentation > Semantic Segmentation >
Etiquetas
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Descubra Live Editor
Cree scripts con código, salida y texto formateado en un documento ejecutable.