The Global Dimensionality of Face Space

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The Global Dimensionality of Face Space
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 derive such compact representations for the object class of human faces. Here, with an interpretation of PCA as a probabilistic model, we employ two objective criteria to study its generalization properties in the context of large frontal-pose face databases. We find that the eigenfaces, the eigenspectrum, and the generalization depend strongly on the ensemble composition and size, with statistics for populations as large as 5500, still not stationary. Further, the assumption of mirror symmetry of the ensemble improves the quality of the results substantially in the low-statistics regime, and is also essential in the high-statistics regime. We employ a perceptual criterion and argue that, even with large statistics, the dimensionality of the PCA subspace necessary for adequate represe...
Penio S. Penev, Lawrence Sirovich
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where FGR
Authors Penio S. Penev, Lawrence Sirovich
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