The Typical Representational Framework

The dominant representational approach that has evolved is descriptive rather than generative. Training images are used to characterize the range of 2-D appearances of objects to be recognized. Although initially very simple modeling methods were used, the dominant method of characterizing appearance has fairly quickly become estimation of the probability density function (PDF) of the image data for the target class.

For instance, given several examples of a target class $\Omega$ in a low-dimensional representation of the image data, it is straightforward to model the probability distribution function $P({\bf x}\vert\Omega)$ of its image-level features ${\bf x}$ as a simple parametric function (e.g., a mixture of Gaussians), thus obtaining a low-dimensional, computationally efficient appearance model for the target class.

Once the PDF of the target class has been learned, we can use Bayes' rule to perform maximum a posteriori (MAP) detection and recognition. The result is typically a very simple, neural-net-like representation of the target class's appearance, which can be used to detect occurrences of the class, to compactly describe its appearance, and to efficiently compare different examples from the same class. Indeed, this representational framework is so efficient that some of the current face recognition methods can process video data at 30 frames per second, and several can compare an incoming face to a database of thousands of people in under one second -- and all on a standard PC!