We show that the successively evaluated features used in a
sliding window detection process to decide about object
presence/absence also contain knowledge about object deformation. We
exploit these detection features to estimate the object
deformation. Estimated deformation is then immediately applied to not
yet evaluated features to align them with the observed image data. In
our approach, the alignment estimators are jointly learned with the
detector. The joint process allows for the learning of each detection
stage from less deformed training samples than in the previous
stage. For the alignment estimation we propose regressors
that approximate non-linear regression functions and compute the
alignment parameters extremely fast.