Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis has been used in the past to derive practically useful compact representations for di erent classes of objects. One major objection to the applicability of PCA is that it invariably leads to global, nontopographic representations that are not amenable to further processing and are not biologically plausible. In this paper we present a new mathematical construction|Local Feature Analysis (LFA)|for deriving local topographic representations for any class of objects. The LFA representations are sparse-distributed and, hence, are e ectively low-dimensional and retain all the advantages of the compact representations of the PCA. But unlike the global eigenmodes, they give a description of objects in terms of statistically derived local features and their positions. We illustrate the theory by using it to extract local features for three ensembles|2D images of faces without background, 3D surfaces of human heads, and nally 2D faces on a background. The resulting local representations have powerful applications in head segmentation and face recognition.
Penev and Atick REF proposed the local feature analysis (LFA) to extract local features by second order statistics.
Local Feature Analysis: A general statistical theory for object representation
Network: Computation In Neural Systems
Mathematics Computer Science