Abstract. Many object classes, including human faces, can bemodeled as a set of characteristic parts arranged in a variable spatial con guration. We introduce a simpli ed model of a deformable object class and derive the optimal detector for this model. However, the optimal detector is not realizable except under special circumstances (independent part positions). A cousin of the optimal detector is developed which uses \soft" part detectors with a probabilistic description of the spatial arrangement of the parts. Spatial arrangements are modeled probabilistically using shape statistics to achieve invariance to translation, rotation, and scaling. Improved recognition performance over methods based on \hard" part detectors is demonstrated for the problem of face detection in cluttered scenes.

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An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features; it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with wmplacated and varied backgmunds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally.