Face recognition by wavelet domain associative memory

We propose a face recognition scheme based on an auto-associative memory (AM) model. Two kinds of AM models are compared, namely, pseudo-inverse memory and radial basis function (RBF) network, and we found that RBF based associative memory is much more efficient. To capture substantial facial features and reduce computational complexity, we use a wavelet transform (WT) to decompose face images and choose the lowest resolution subband coefficients for face representation. Results indicate that the modular scheme yields accurate recognition on the widely used XM2VTS face database and Olivetti Research Laboratory (ORL) face database