Detection of faces and facial landmarks using iconic filter banks

We describe a novel and general approach for the detection of objects, specifically faces and facial landmarks. The approach is based on biologically motivated image representation and classification schemes. Early processing related to feature extraction involves retinal sampling along non-linear lattices and micro saccades, while classification is done using retinal filter banks implemented via self-organizing feature maps (SOFM). The optimal set of face, eye pair, eye, nose, and mouth feature models, respectively, is found by an enhanced SOFM approach using cross-validation and corrective training. Experimental results on a data set of over 400 images prove the feasibility of our approach.

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