1. A method comprising: classifying a sub-window image into a first feature point candidate recommendation image and a first feature point candidate non-recommendation image by using first selected feature patterns, wherein a classifier includes cascaded strong classifiers, and a strong classifier in a former stage among the cascaded strong classifiers classifies the sub-window image into the first feature point candidate recommendation image and the first feature point candidate non-recommendation image using a smaller number of the first feature patterns, as compared with a strong classifier in a latter stage; generating first feature point candidate position information of the first feature point candidate recommendation image; re-classifying the sub-window image classified as the first feature point candidate non-recommendation image into a second feature point candidate recommendation image and a second feature point candidate non-recommendation image by using second selected feature patterns; generating second feature point candidate position information of the second feature point candidate recommendation image; storing the first feature point candidate position information and pieces of the second feature point candidate position information for sub-window images of a facial image; forming clusters by performing clustering in view of the first feature point candidate position information and the second feature point candidate position information; and generating position information of a certain representative position of the first feature point candidate position information and the second feature point candidate position information forming a largest cluster among the clusters as feature point position information.