An integrated framework for evaluating the performance of age progression algorithms

Abstract

Facial age progression can play an important role in biometric authentication as it enables the long-Term person identification based on age progressed facial renderings. In this paper, a systematic evaluation approach that can be used for assessing the performance of age progression algorithms is presented. The proposed method relies on the use of a dedicated dataset in conjunction with the development and use of machine-based performance evaluation metrics that allow the assessment of the intensity of aging effects added on a face along with an assessment of the identity preservation in age progressed images. The proposed performance evaluation framework can form the basis of implementing comprehensive comparative performance evaluation between different age progression methodologies, allowing in that way the evolution of the best algorithms.