Locating facial features using SOFM

We describe a novel and general approach for the detection of facial features such as the eyes. The approach is based on biologically motivated processing and classification schemes. The processing involves retinal sampling along P-type lattices and micro saccades, while classification is done using the self-organizing feature map (SOFM). The optimal set of eye templates is found by an enhanced SOFM approach using cross-validation training. Experimental results are presented to prove the feasibility of our approach