FACE MORPH AGE PROGRESSION APPLICATIONS GENERATOR
FACE MORPH AGE PROGRESSION APPLICATIONS SOFTWARE

The proposed model consists of one generator with three-level encoders and three levels of discriminators with an age and a gender classifier on top of each discriminator. In this work, we propose a cycle-consistent loss based method for face aging with wavelet-based multi-level facial attributes extraction from both generator and discriminators. Previous works usually rely on an extra pre-trained module for identity preserving and multi-level discriminators for fine-grained features extraction. Age accuracy and identity preserving are two important indicators for face aging. Google and Intel Corporation funded the research.Face aging has received increasing attention from the computer vision community due to wide applications in the real world. “The aging process is one of many dimensions to consider.” “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. 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.

FACE MORPH AGE PROGRESSION APPLICATIONS SOFTWARE

The automatic age-progression software can run on a standard computer and takes about 30 seconds to generate results for one face. 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.īut 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. Perhaps the most common application of age progression work is for rendering older versions of missing children. A single photo of a child (far left) is age progressed (left in each pair) and compared to actual photos of the same person at the corresponding age (right in each pair).

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. Real-life photos of children are difficult to age-progress, partly due to variable lighting, shadows, funny expressions, and even milk moustaches.

Using one photo of a 3-year-old, the software automatically renders images of his face at multiple ages while keeping his identity (and the milk moustache). “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.” “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. 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. The researchers tested their rendered images against those of 82 actual people photographed over a span of years. The changes are then applied to a new child’s photo to predict how he will appear for any subsequent age up to 80. More specifically, the software determines the average pixel arrangement from thousands of random internet photos of faces in different age and gender brackets.Īn algorithm then finds correspondences between the averages from each bracket and calculates the average change in facial shape and appearance between ages. 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. 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 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. “We took photos of children in completely unrestrained conditions and found that our method works remarkably well.” “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. The new technique is the first that works with variable lighting, expressions, and poses, researchers say. 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.

Face morph age progression applications