- Age face make me young helps you to transform your face into young face. Face age editor app is face aging booth machine for you. You can take photo from your gallery or take one picture from.
- Considering the expensive cost of collecting paired datasets, CAAE (Conditional Adversarial Autoencoder) is designed for face aging task without paired samples and first achieves face age.
- We formulate facial age synthesis as an unsupervised multi-domain image-to-image translation problem, and devise a novel generative framework using only a single generative adversarial network, dubbed FaceGAN which synthesizes photo-realistic face images with aging effects with unpaired samples and achieves face age progression and regression in a holistic framework.
- Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age.
Posted in Pix / Tagged age progression, age regression, artists, botticelli, el greco, ethnicity, face, masters, modigliani, renderings, transformer Post navigation An Ode to Hill and Adamson.
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- Date:
- October 3, 2008
- Source:
- University of Illinois at Urbana-Champaign
- Summary:
- Like an age-guesser at a carnival, new computer software can fairly accurately estimate a person's age. But, unlike age-guessers, who can view a person's body, the software works by examining only the person's face.
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People who hope to keep their age a secret won't want to go near a computer running this software.
Like an age-guesser at a carnival, computer software being developed at the University of Illinois can fairly accurately estimate a person's age. But, unlike age-guessers, who can view a person's body, the software works by examining only the person's face.
'Age-estimation software is useful in applications where you don't need to specifically identify someone, such as a government employee, but would like to know their age,' said Thomas S. Huang, the William L. Everitt Distinguished Professor of Electrical and Computer Engineering at the U. of I.
For example, age-recognition algorithms could stop underage drinkers from entering bars, prevent minors from purchasing tobacco products from vending machines, and deny children access to adult Web sites, said Huang, who leads the Image Formation and Processing group at the university's Beckman Institute.
Estimating someone's age is not an easy task, even for a computer. That's partly because the aging process is determined not only by a person's genetic makeup, but by many other factors as well, including health, location and living conditions.
'Human faces do convey a significant amount of information, however, and provide important visual cues for estimating age,' Huang said. 'Facial attributes, such as expression, gender and ethnic origin, play a crucial role in our image analysis.'
Consisting of three modules – face detection, discriminative manifold learning, and multiple linear regression – the researchers' age-estimation software was trained on a database containing photos of 1,600 faces.
The software can estimate ages from 1 year to 93 years. The software's accuracy ranges from about 50 percent when estimating ages to within 5 years, to more than 80 percent when estimating ages to within 10 years. The accuracy can be improved by additional training on larger databases of faces, Huang said.
In addition to performing tasks such as security control and surveillance monitoring, age-estimation software also could be used for electronic customer relationship management.
For example, a camera snapping photos of customers could collect demographic data – such as how many adult men and women buy burgers, or what percentage of teenagers purchase a particular soft drink.
Or, combined with algorithms that identify a person's sex, age-estimation software could help target specific audiences for specific advertisements. For example, a store display might advertise a new automobile or boat as a man walks by, or new clothing or cosmetics as a woman walks by.
'All of this can be done without violating anyone's privacy,' Huang said. 'Our software does not identify specific individuals. It just estimates their ages.'
Huang is affiliated with the university's Center for Advanced Study, Coordinated Science Laboratory, Information Trust Institute, and department of computer science.
Funding was provided by the National Science Foundation and the Intelligence Advanced Research Projects Activity. The researchers published their findings in the two journals IEEE Transactions on Multimedia and IEEE Transactions on Image Processing in 2008.
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Materials provided by University of Illinois at Urbana-Champaign. Note: Content may be edited for style and length.
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'Our extensive user studies demonstrated age progression results that are so convincing that people can’t distinguish them from reality,' says Steven Seitz. 'When shown images of an age-progressed child photo and a photo of the same person as an adult, people are unable to reliably identify which one is the real photo.' (Credit: Scott Sherrill-Mix/Flickr)
New age-progression software generates images of a young child’s face as it ages through a lifetime, and does so in less than a minute.
The new technique is the first that works with variable lighting, expressions, and poses, researchers say.
“Aging photos of very young children from a single photo is considered the most difficult of all scenarios, so we wanted to focus specifically on this very challenging case,” says Ira Kemelmacher-Shlizerman, assistant professor of computer science and engineering at the University of Washington.
“We took photos of children in completely unrestrained conditions and found that our method works remarkably well.”
The research team has posted a paper on the new technique and will present its findings at the June IEEE Computer Vision and Pattern Recognition conference in Columbus, Ohio.
Thousands of faces
The shape and appearance of a baby’s face—and variety of expressions—often change drastically by adulthood, making it hard to model and predict that change. The new technique leverages the average of thousands of faces of the same age and gender, then calculates the visual changes between groups as they age to apply those changes to a new person’s face.
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More specifically, the software determines the average pixel arrangement from thousands of random internet photos of faces in different age and gender brackets.
Facial Age Progression Software
An algorithm then finds correspondences between the averages from each bracket and calculates the average change in facial shape and appearance between ages. The changes are then applied to a new child’s photo to predict how he will appear for any subsequent age up to 80.
Real or not?
The researchers tested their rendered images against those of 82 actual people photographed over a span of years. In an experiment asking random users to identify the correct aged photo for each example, they found that users picked the automatically rendered photos about as often as the real-life ones.
“Our extensive user studies demonstrated age progression results that are so convincing that people can’t distinguish them from reality,” says coauthor Steven Seitz, professor of computer science and engineering. “When shown images of an age-progressed child photo and a photo of the same person as an adult, people are unable to reliably identify which one is the real photo.”
Real-life photos of children are difficult to age-progress, partly due to variable lighting, shadows, funny expressions, and even milk moustaches. To compensate for these effects, the algorithm first automatically corrects for tilted faces, turned heads, and inconsistent lighting, then applies the computed shape and appearance changes to the new child’s face.
Photo Age Regression Software Free
Missing children images
Perhaps the most common application of age progression work is for rendering older versions of missing children. These renderings usually are created manually by an artist who uses photos of the child as well as family members, and editing software to account for common changes to a child’s face as it ages, including vertical stretching, wrinkles, and a longer nose.
But the process takes time, and it’s significantly harder to produce an accurate image for children younger than age 5, when facial features more closely resemble that of a baby.
Facial Age Regression Software
The automatic age-progression software can run on a standard computer and takes about 30 seconds to generate results for one face. While the method considered gender and age, the researchers hope to incorporate other identifiers such as ethnicity, and cosmetic factors such as hair whitening and wrinkles to build a robust enough method for representing every human face.
“I’m really interested in trying to find some representation of everyone in the world by leveraging the massive amounts of captured face photos,” Kemelmacher-Shlizerman says. “The aging process is one of many dimensions to consider.”
Google and Intel Corporation funded the research.
Source: University of Washington