Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning

TL;DR

This paper introduces SDAP, a deep learning model that personalizes face aging paths using inverse reinforcement learning, improving face aging synthesis and verification across multiple datasets.

Contribution

The paper proposes a novel SDAP model combining probabilistic modeling and inverse reinforcement learning for personalized face aging, allowing multiple inputs and efficient in-the-wild synthesis.

Findings

Achieves state-of-the-art results on FG-NET, MORPH, AGFW, and CACD datasets.

Demonstrates superior performance in face aging synthesis and cross-age verification.

Excels in large-scale Megaface challenge evaluations.

Abstract

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.