Next Article in Journal
Exploring Mobile Terminal Continuance Usage from Customer Value Perspective
Previous Article in Journal
Blind Robust 3D Mesh Watermarking Based on Mesh Saliency and Wavelet Transform for Copyright Protection
Article Menu

Export Article

Open AccessArticle
Information 2019, 10(2), 69; https://doi.org/10.3390/info10020069

Bidirectional Face Aging Synthesis Based on Improved Deep Convolutional Generative Adversarial Networks

1,2,*
,
1,2,*
,
1,2
,
1,2
and
1,2
1
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
2
Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Wuhan University of Technology, Ministry of Education, Wuhan 430070, China
*
Authors to whom correspondence should be addressed.
Received: 10 January 2019 / Revised: 29 January 2019 / Accepted: 15 February 2019 / Published: 18 February 2019
Full-Text   |   PDF [11635 KB, uploaded 23 February 2019]   |  

Abstract

The use of computers to simulate facial aging or rejuvenation has long been a hot research topic in the field of computer vision, and this technology can be applied in many fields, such as customs security, public places, and business entertainment. With the rapid increase in computing speeds, complex neural network algorithms can be implemented in an acceptable amount of time. In this paper, an optimized face-aging method based on a Deep Convolutional Generative Adversarial Network (DCGAN) is proposed. In this method, an original face image is initially mapped to a personal latent vector by an encoder, and then the personal potential vector is combined with the age condition vector and the gender condition vector through a connector. The output of the connector is the input of the generator. A stable and photo-realistic facial image is then generated by maintaining personalized facial features and changing age conditions. With regard to the objective function, the single adversarial loss of the Generated Adversarial Network (GAN) with the perceptual similarity loss is replaced by the perceptual similarity loss function, which is the weighted sum of adversarial loss, feature space loss, pixel space loss, and age loss. The experimental results show that the proposed method can synthesize an aging face with rich texture and visual reality and outperform similar work. View Full-Text
Keywords: face-aging synthesis; GAN; DCGAN; latent vector; perceptual similarity loss face-aging synthesis; GAN; DCGAN; latent vector; perceptual similarity loss
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Liu, X.; Zou, Y.; Xie, C.; Kuang, H.; Ma, X. Bidirectional Face Aging Synthesis Based on Improved Deep Convolutional Generative Adversarial Networks. Information 2019, 10, 69.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top