Hello,

We noticed you're browsing in private or incognito mode.

To continue reading this article, please exit incognito mode or log in.

Not an Insider? Subscribe now for unlimited access to online articles.

Intelligent Machines

Neural Network Learns to Synthetically Age Faces, and Make Them Look Younger, Too

Deep-learning machines can make faces look older but often lose their identity in the process. Now computer scientists have solved this problem.

The way we age is deeply fascinating. Indeed, knowing how our faces will look in 20, 30, or 40 years’ time is a trick that many would find captivating.

A number of techniques exist that can do this. But they are time-consuming and hence expensive. So a cheap and quick way to age faces in photographs would be a handy trick.

Enter Grigory Antipov from Orange Labs in France and a couple of pals who have developed a deep-learning machine that can do the job with ease. Not only can their system make young faces look older, it can make older faces look younger.

A couple of recent developments have made their task easier. In recent years, computer scientists have built deep-learning machines that are able to modify faces in various different but realistic ways. This approach can create realistic synthetic faces that look older.

However, there is a problem. In making faces look older, these deep-learning machines often lose the person’s identity in the process. So the individual looks older but can no longer be identified.

Antipov and co have come up with a way to solve that problem. Their approach involves two deep-learning machines that work together—a face generator and a face discriminator. Both machines learn what faces look like as they age by analyzing photographs of people in the age groups 0-18, 19- 29, 30-39, 40-49, 50-59, and 60+ years old.

In total, the machines were trained on 5,000 faces in each group taken from the Internet Movie Database and from Wikipedia and then labeled with the person’s age. In this way, the machine learns the characteristic signature of faces in each age group. It is this abstract signature that the face generator can then apply to other faces to make them look the same age.

However, applying this signature can sometimes cause a person’s identity to be lost. So the second deep-learning machine—the face discriminator—looks at the synthetically aged face to see whether the original identity can still be picked out. If it can’t, the image is rejected.

Antipov and co call their process Age Conditional Generative Adversarial Network—adversarial because the deep-learning machines work in opposition.

The results make for impressive reading. The team applied the technique to 10,000 faces from the IMDB-Wikipedia database that they hadn’t used for training. They then tested the before and after images using software called OpenFace which can tell whether two images show the same person or not. This spotted the same face more than 80 percent of the time, compared to about 50 percent of the time for other face-aging techniques.

And, of course, the technique not only ages young faces but creates younger versions of older faces, too.

There is an obvious test the team has not done. Presumably, it’s possible to compare faces that have been made younger synthetically with pictures of the same face taken when the individual was actually younger. That would be a good test of how accurate the technique is and perhaps a task for the future.

Antipov and co say their technique could be used in applications such as helping identify people who have been missing for many years. It might also be a lot of fun to play with, should they choose to make their algorithm public.

Ref: arxiv.org/abs/1702.01983: Face Aging with Conditional Generative Adversarial Networks

Keep up with the latest in deep learning at EmTech Digital.
Don't be left behind.

March 25-26, 2019
San Francisco, CA

Register now
More from Intelligent Machines

Artificial intelligence and robots are transforming how we work and live.

Want more award-winning journalism? Subscribe and become an Insider.
  • Insider Plus {! insider.prices.plus !}* Best Value

    {! insider.display.menuOptionsLabel !}

    Everything included in Insider Basic, plus the digital magazine, extensive archive, ad-free web experience, and discounts to partner offerings and MIT Technology Review events.

    See details+

    Print + Digital Magazine (6 bi-monthly issues)

    Unlimited online access including all articles, multimedia, and more

    The Download newsletter with top tech stories delivered daily to your inbox

    Technology Review PDF magazine archive, including articles, images, and covers dating back to 1899

    10% Discount to MIT Technology Review events and MIT Press

    Ad-free website experience

  • Insider Basic {! insider.prices.basic !}*

    {! insider.display.menuOptionsLabel !}

    Six issues of our award winning print magazine, unlimited online access plus The Download with the top tech stories delivered daily to your inbox.

    See details+

    Print Magazine (6 bi-monthly issues)

    Unlimited online access including all articles, multimedia, and more

    The Download newsletter with top tech stories delivered daily to your inbox

  • Insider Online Only {! insider.prices.online !}*

    {! insider.display.menuOptionsLabel !}

    Unlimited online access including articles and video, plus The Download with the top tech stories delivered daily to your inbox.

    See details+

    Unlimited online access including all articles, multimedia, and more

    The Download newsletter with top tech stories delivered daily to your inbox

/3
You've read of three free articles this month. for unlimited online access. You've read of three free articles this month. for unlimited online access. This is your last free article this month. for unlimited online access. You've read all your free articles this month. for unlimited online access. You've read of three free articles this month. for more, or for unlimited online access. for two more free articles, or for unlimited online access.