9. The method according to claim 1 , wherein the method further comprises a step of extracting a set of age sensitive features from a facial image, wherein the set of age sensitive features is fed to auxiliary demographics class machines, wherein each of the auxiliary demographics class machines computes an auxiliary class likelihood output of the facial image belonging to the auxiliary demographics class and age outputs, and whereby the set of age sensitive features are computed by applying a set of high frequency filters that are designed to extract the positions and shapes of facial boundary, facial features, and to extract facial wrinkles.
10. The method according to claim 9 , wherein the method further comprises a step of training a plurality of learning machines using the age sensitive features as training data to classify the age of the facial image, wherein a desired age vector and auxiliary class likelihood output is calculated for each training data, and wherein each training data corresponds to one of the auxiliary demographics classes.