A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry

Many object classes including human faces can be modeled as a set of characteristic parts arranged in a variable spatial con gu ration 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 indepen 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 probabilisti cally 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