  In this paper, the problem of automatic Gabor wavelet selection for face
recognition is tackled by introducing an automatic algorithm based on Parallel
AdaBoosting method. Incorporating mutual information into the algorithm leads
to the selection procedure not only based on classification accuracy but also
on efficiency. Effective image features are selected by using properly chosen
Gabor wavelets optimised with Parallel AdaBoost method and mutual information
to get high recognition rates with low computational cost. Experiments are
conducted using the well-known FERET face database. In proposed framework,
memory and computation costs are reduced significantly and high classification
accuracy is obtained.
