Reducing the Dimensionality of Face Space in a Sparse Distributed Local-Features Representation
نویسنده
چکیده
Low-dimensional representations of sensory signals are key to solving many of the computational problems encountered in high-level vision. Principal Component Analysis (PCA) has been used in the past to provide a global representation with a dimensionality as small as 400 of both images of human faces and their probabilities. Local Feature Analysis (LFA) is a biologically-motivated method that has been shown to preserve all the desirable properties of PCA, and also build a more intuitive and efficient, sparsedistributed representation of faces in terms of flexible templates of local features. Here we study the properties of LFA as a probability model, and show that the dimensionality of the LFA representation is three times smaller than that of PCA. We discuss the implications for both compression and recognition algorithms.
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