Face image based age categorization is an approach to classify face images into one of several pre-defined age-groups. It is challenging because the aging variation is specific to a given individual and is determined by not only the person's gene, but also by many external factors, such as exposure, weather conditions (e.g. ambient humidity), health, gender, living style and living location. Age categorization is a multiclass problem. One of the AdaBoost or SVM extensions for solving the multiclass problem is the combination of the method of error-correcting output codes (ECOC) with boosting using a decision tree based classifier or binary SVM classifier. In this paper, we apply this extension to solve the age categorization problem. Gabor and LBP aging features are extracted and combined at the feature level to represent the face images. Experimental results on FG-NET and Morph database are reported to demonstrate its effectiveness and robustness. The ECOC can achieve nearly similar results when it was combined with AdaBoost or SVM. However, ECOC plus AdaBoost is much faster than ECOC plus SVM. The results obtained using the fused LBP and Gabor features are better than the one when using either LBP or Gabor alone.
['Jian-Gang Wang', 'Wei-Yun Yau', 'Hee Lin Wang']
Age categorization via ECOC with fused gabor and LBP features