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

Authors
  • Duong, Chi Nhan1
  • Quach, Kha Gia1
  • Luu, Khoa2
  • Le, T. Hoang Ngan3
  • Savvides, Marios3
  • Bui, Tien D.1
  • 1 Concordia University, Department of Computer Science and Software Engineering, Montréal, Québec, Canada , Montréal (Canada)
  • 2 University of Arkansas, Computer Science and Computer Engineering Department, Fayetteville, AR, USA , Fayetteville (United States)
  • 3 Carnegie Mellon University, CyLab Biometrics Center and the Department of Electrical and Computer Engineering, Pittsburgh, PA, USA , Pittsburgh (United States)
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Feb 27, 2019
Volume
127
Issue
6-7
Pages
957–971
Identifiers
DOI: 10.1007/s11263-019-01165-5
Source
Springer Nature
Keywords
License
Yellow

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.

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