A.Jonathan Howell, Hilary Buxton
Preprocessing of face images was performed to mimic the effects of receptive field functions found at various stages of the human vision system. These were then used as input representations to Radial Basis Function (RBF) networks that learnt to classify and generalise over different views for a standard face recognition task. Two main organisations of the RBF networks (standard and face unit) and two main types of pre-processing (Difference of Gaussian filtering and Gabor wavelet analysis) were compared. Quantitative and qualitative differences in these schemes are described and conclusions drawn about the best approach for our face recognition problem using low resolution images.
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