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International Journal of Computer Vision

, Volume 127, Issue 6–7, pp 957–971 | Cite as

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

  • Chi Nhan DuongEmail author
  • Kha Gia Quach
  • Khoa Luu
  • T. Hoang Ngan Le
  • Marios Savvides
  • Tien D. Bui
Article
  • 208 Downloads

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.

Keywords

Face age progression Deep generative models Face aging Subject-dependent deep aging Tractable graphical probabilistic models 

Notes

Supplementary material

11263_2019_1165_MOESM1_ESM.pdf (5.1 mb)
Supplementary material 1 (pdf 5255 KB)

Supplementary material 2 (mp4 119181 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontréalCanada
  2. 2.Computer Science and Computer Engineering DepartmentUniversity of ArkansasFayettevilleUSA
  3. 3.CyLab Biometrics Center and the Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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