1. A method of detecting an orientation of a digital image using statistical classifier techniques, comprising: using a processor; acquiring a digital image; detecting an object within the digital image; cropping the digital image including selecting an object region that includes said object within the digital image and excludes one or more regions of the digital image outside of said object region to obtain a cropped image including the object region; applying a set of object detection classifiers to the cropped image in a first orientation and determining a first level of match between said cropped image at said first orientation and said classifiers; rotating said cropped image to a second orientation to obtain a rotated image, applying the classifiers to said rotated image at said second orientation, and determining a second level of match between said rotated image at said second orientation and said classifiers; comparing said first and second levels of match between said classifiers and said cropped image and between said classifiers and said rotated image, respectively; and determining which of the first orientation and the second orientations has a greater probability of being a correct orientation based on which of the first and second levels of match, respectively, comprises a higher level of match.