  This paper presents a novel Subject-dependent Deep Aging Path (SDAP), which
inherits the merits of both Generative Probabilistic Modeling and Inverse
Reinforcement Learning to model the facial structures and the longitudinal face
aging process of a given subject. The proposed SDAP is optimized using
tractable log-likelihood objective functions with Convolutional Neural Networks
(CNNs) based deep feature extraction. Instead of applying a fixed aging
development path for all input faces and subjects, SDAP is able to provide the
most appropriate aging development path for individual subject that optimizes
the reward aging formulation. Unlike previous methods that can take only one
image as the input, SDAP further allows multiple images as inputs, i.e. all
information of a subject at either the same or different ages, to produce the
optimal aging path for the given subject. Finally, SDAP allows efficiently
synthesizing in-the-wild aging faces. The proposed model is experimented in
both tasks of face aging synthesis and cross-age face verification. The
experimental results consistently show SDAP achieves the state-of-the-art
performance on numerous face aging databases, i.e. FG-NET, MORPH, AginG Faces
in the Wild (AGFW), and Cross-Age Celebrity Dataset (CACD). Furthermore, we
also evaluate the performance of SDAP on large-scale Megaface challenge to
demonstrate the advantages of the proposed solution.
