  We perform preliminary studies on a large longitudinal face database
MORPH-II, which is a benchmark dataset in the field of computer vision and
pattern recognition. First, we summarize the inconsistencies in the dataset and
introduce the steps and strategy taken for cleaning. The potential implications
of these inconsistencies on prior research are introduced. Next, we propose a
new automatic subsetting scheme for evaluation protocol. It is intended to
overcome the unbalanced racial and gender distributions of MORPH-II, while
ensuring independence between training and testing sets. Finally, we contribute
a novel global framework for age estimation that utilizes posterior
probabilities from the race classification step to compute a racecomposite age
estimate. Preliminary experimental results on MORPH-II are presented.
