1. A method for classifying a candidate red-eye object in a digital image, the method comprising the acts of: i) selecting a candidate red-eye object in a digital image; ii) determining a search scale set and a search region for the candidate red-eye object where an eye object may reside; iii) determining a number of subwindows that satisfy an AdaBoost classifier, the number denoted as a vote; iv) determining the maximum size of the subwindows that satisfy the AdaBoost classifier; v) calculating a normalized threshold by multiplying a predetermined constant threshold by the maximum size calculated at act iv); vi) determining that the vote is greater than the normalized threshold; and vii) transforming the candidate red-eye object into a true red-eye object.