Face Localization via Shape Statistics
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.
© 1995 University of Zurich. This work was supported by the Center for Neuromorphic Systems Engineering as a part of the National Science Foundation (NSF) Engineering Research Center Program, by the California Trade and Commerce Agency, Office of Strategic Technology, by an NSF National Young Investigator award, and by a grant from Intel. We are very grateful to Jitendra Malik for useful discussions and Steve Evans for providing references to the shape statistics literature.
Published - Burl_face_localizationZurich95.pdf