Attention and pattern detection using sensory and reactive control mechanisms

We introduce a biologically motivated low level model of visual attention and saccade generation based on data-driven dynamic processes governing foveation and recognition of object primitives. The approach consists of two major processing pathways, magno- (M) and parvocellular (P), and it employs: 1) retinal sampling, 2) active foveation, and 3) low-level (“coarse”) recognition mechanisms. The M (“where”) channel, responsible for object localization and corresponding reflexive saccades, feeds the P channel with salient locations for pattern detection. The P (“what”) channel matches the image locations (“sensory”) channel against previously interpreted and possibly labelled them. The P (“reactive”) channel also generates the conditional saccades needed to collect additional information as it might be appropriate for full pattern interpretation. Simulation results, in the context of face recognition and using a large data set of 200 subjects, demonstrate the feasibility of our approach