A probabilistic approach to object recognition using local photometry and global geometry

Abstract

Many object classes, including human faces, can be modeled as a set of characteristic parts arranged in a variable spatial configuration. We introduce a simplified 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 (indepem dent 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.