Face Recognition using Radial Basis Function Neural Networks

This paper presents experiments using an adaptive learning compo nent based on Radial Basis Function RBF networks to tackle the unconstrained face recognition problem using low resolution video in formation Firstly we performed preprocessing of face images to mimic the e ects of receptive eld functions found at various stages of the hu man vision system These were then used as input representations to RBF networks that learnt to classify and generalise over di erent views for a standard face recognition task Two main types of preprocessing Di erence of Gaussian ltering and Gabor wavelet analysis are com pared Secondly we provide an alternative face unit RBF network model that is suitable for large scale implementations by decomposi tion of the network which avoids the unmanagability of neural net works above a certain size Finally we show the D shift scale and y axis rotation invariance properties of the standard RBF network Quantitative and qualitative di erences in these schemes are described and conclusions drawn about the best approach for real applications to address the face recognition problem using low resolution images