Singular Value Decomposition, Eigenfaces, and 3D Reconstructions

Abstract

Abstract. Singular value decomposition (SVD) is one of the most important and useful factorizations in linear algebra. We describe how SVD is applied to problems involving image processing-in particular, how SVD aids the calculation of so-called eigenfaces, which provide an efficient representation of facial images in face recognition. Although the eigenface technique was developed for ordinary grayscale images, the technique is not limited to these images. Imagine an image where the different shades of gray convey the physical threedimensional structure of a face. Although the eigenface technique can again be applied, the problem is finding the three-dimensional image in the first place. We therefore also show how SVD can be used to reconstruct three-dimensional objects from a two-dimensional video stream.

Linear algebra for vision-based surveillance in heavy industry - convergence behavior case study

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Incremental learning of bidirectional principal components for face recognition

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“…Detecting the human face is more complicated than other objects because of its dynamic nature with many forms and colors [1]. The tough part of facial recognition is isolating it from the background.…”