An experimental comparison of preprocessing methods for age classification

Youngwoo Yoon, Ho-Sub Yoon, Jaeyeon Lee

Abstract: This paper investigates image transformations as preprocessing steps that can be applied toward a state-of-the-art age classification framework of manifold learning. We report on the experimental results of four different preprocessing methods in terms of classification accuracy using a large training and test face database. The use of histograms of oriented gradient (HOG) descriptors increases the classification accuracy from 46 to 75%. Robustness to the rotation and translation is also tested.