Abstract

In this paper, a face localization system is proposed in which local detectors are coupled with a statistical model of the spatial arrangement of facial features to yield robust performance. The outputs from the local detectors are treated as candidate locations and constellations are formed from these. The effects of translation, rotation, and scale are eliminated by mapping to a set of shape variables. The constellations are then ranked according to the likelihood that the shape variables correspond to a face versus an alternative model. Incomplete constellations, which occur when some of the true features are missed, are handled in a principled way. 1 Introduction The problem of face recognition has received considerable attention in the literature [11, 24, 21, 4, 19, 17, 22, 10]; however, in most of these studies, the faces were either embedded in a benign background or were assumed to have been pre-segmented. For any of these recognition algorithms to work in realworld applicati...

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