Recognizing faces from their parts

In many situations, only some parts of an object are visible while other parts are occluded. In other situations, information about an object is available piecemeal as the parts are scanned sequentially, such as when eye-motions are used to explore an object. Part information is also crucially important for objects with articulating parts, or with removable parts. In all of these cases, the sensor-scanner system must divide an object into subcomponents, and must also be able to integrate the part-information using appropriate data concerning the spatial relationships among the parts as well as the temporal scan sequences. This work describes how such issues are addressed in recognizing human faces from their parts using a neural network approach. Parallels are drawn between neurophysiological and psychophysical experiments, as well as deficits in visual object recognition. This work extends our existing modular system, developed for learning and recognizing 3D objects from multiple views, by investigating the capabilities which need to be augmented for coping with objects which are represented hierarchically. The ability of the previous system to learn and recognize 3D objects invariant to their apparent size, orientation, position, perspective projection, and 3D pose serves as a strong foundation for the extension to more complex 3D objects.