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Hierarchical Face Aging Through Disentangled Latent Characteristics

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12348)

Abstract

Current age datasets lie in a long-tailed distribution, which brings difficulties to describe the aging mechanism for the imbalance ages. To alleviate it, we design a novel facial age prior to guide the aging mechanism modeling. To explore the age effects on facial images, we propose a Disentangled Adversarial Autoencoder (DAAE) to disentangle the facial images into three independent factors: age, identity and extraneous information. To avoid the “wash away” of age and identity information in face aging process, we propose a hierarchical conditional generator by passing the disentangled identity and age embeddings to the high-level and low-level layers with class-conditional BatchNorm. Finally, a disentangled adversarial learning mechanism is introduced to boost the image quality for face aging. In this way, when manipulating the age distribution, DAAE can achieve face aging with arbitrary ages. Further, given an input face image, the mean value of the learned age posterior distribution can be treated as an age estimator. These indicate that DAAE can efficiently and accurately estimate the age distribution in a disentangling manner. DAAE is the first attempt to achieve facial age analysis tasks, including face aging with arbitrary ages, exemplar-based face aging and age estimation, in a universal framework. The qualitative and quantitative experiments demonstrate the superiority of DAAE on five popular datasets, including CACD2000, Morph, UTKFace, FG-NET and AgeDB.

Keywords

Facial age analysis Variational autoencoder 

Notes

Acknowledgement

This work is partially funded by National Natural Science Foundation of China (Grant No. U1836217) and Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (NO.2019JZZY010119).

Supplementary material

504435_1_En_6_MOESM1_ESM.pdf (2.7 mb)
Supplementary material 1 (pdf 2792 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Center for Research on Intelligent Perception and Computing, NLPR, CASIABeijingChina
  2. 2.Center for Excellence in Brain Science and Intelligence TechnologyCASBeijingChina
  3. 3.School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
  4. 4.Artificial Intelligence ResearchCASJiaozhou, QingdaoChina

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