Attentional Processes in Correspondence-Based Object Recognition

We show here that a model for invariant object recognition described earlier [1] naturally displays effects of space based and object based attention. The network consists of three functional layers: an input layer for image representation, an intermediate assembly layer for recurrent information integration, and a gallery layer for memory storage (see figure). Each layer consists of cortical columns as functional building blocks that are modeled in accordance with recent experimental findings. Connections between input and assembly layer are modulated by control units on a fast time scale to allow detection of objects in the input image and position invariant mapping to the normalized assembly layer. Recurrent information exchange between assembly and gallery layer leads to recognition. Tests on standard benchmark databases show competitive performance for face recognition.