This paper is a comparative study of three recently proposed algorithms for face recognition: eigenface, autoassociation and classification neural nets, and elastic matching. After these algorithms were analyzed under a common statistical decision framework, they were evaluated experimentally on four individual data bases, each with a moderate subject size, and a combined data base with more than a hundred different subjects. Analysis and experimental results indicate that the eigenface algorithm, which is essentially a minimum distance classifier, works well when lighting variation is small. Its performance deteriorates significantly as lighting variation increases. The elastic matching algorithm, on the other hand, is insensitive to lighting, face position, and expression variations and therefore is more versatile. The performance of the autoassociation and classification nets is upper bounded by that of the eigenface but is more difficult to implement in practice.

An object recognition system based on the dynamic link architecture, an extension to classical artificial neural networks (ANNs), is presented. The dynamic link architecture exploits correlations in the fine-scale temporal structure of cellular signals to group neurons dynamically into higher-order entities. These entities represent a rich structure and can code for high-level objects. To demonstrate the capabilities of the dynamic link architecture, a program was implemented that can recognize human faces and other objects from video images. Memorized objects are represented by sparse graphs, whose vertices are labeled by a multiresolution description in terms of a local power spectrum, and whose edges are labeled by geometrical distance vectors. Object recognition can be formulated as elastic graph matching, which is performed here by stochastic optimization of a matching cost function. The implementation on a transputer network achieved recognition of human faces and office objects from gray-level camera images. The performance of the program is evaluated by a statistical analysis of recognition results from a portrait gallery comprising images of 87 persons.>

A neural system is presented for invariant object recognition. Its flexibility is demonstrated with freely taken camera images of human faces. The system is an application of the dynamic link architecture, which owes its strength to an enhancement of tradition neural networks by a new kind of variable to express the hierarchical grouping of neurons. This capability is used to group primitive local feature detectors (Gabor-based wavelets) into composite feature detectors (jets) and to preserve neighborhood relationships between jets when they lose position information on the way from the image domain to the object domain. Due to the potential for grouping, objects can be represented as attributed graphs, with jets serving as attributes. Recognition is formulated as graph matching and is implemented as a topologically constrained diffusion of image-object links. A hierarchical sequence of matches, from low-frequency components of jets to high-frequency components, is used. Size invariance is achieved by interposing diffusion steps in magnification space. The system is implemented on a network of transputers