Global and local feature based multi-classifier A-stack model for aging face identification

The problem of time validity of biometric models has received only a marginal attention from researchers. Actual and up-to-date at the time of their creation, extracted features and models relevant to a person's face may eventually become outdated, leading to a failure in the face identification task. If physical characteristics of the individual change over time, their classification model has to be updated. In this paper we present a mutli-classifier A-stack scheme, which is based on the concept of classifier stacking and makes use of the age information and scores of multiple baseline classifiers, in order to improve the identification performance during age progression. Our experiments on the MORPH database show that the use of the proposed multi-classifier stacking fusion allows for improving the identification accuracy as opposed to the baseline classifier and single-classifier A-stack method.