Face morph age progression software#
Face morph age progression series#

Face morph age progression series#

As the series progresses, you see apparent aging in the subject in supposedly real time. A decent morphing software, a series of images, then you specify pair points on each photo. This research was funded by Google and Intel Corp.ДЇor more information, contact Kemelmacher-Shlizerman at or 20.Actually, it's pretty easy. вЂњThe aging process is one of many dimensions to consider.вЂќ вЂњIвЂ™m really interested in trying to find some representation of everyone in the world by leveraging the massive amounts of captured face photos,вЂќ Kemelmacher-Shlizerman said. While this method considered gender and age, the research team that also includes UW doctoral student Supasorn Suwajanakorn hopes 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 software#

The automatic age-progression software can run on a standard computer and takes about 30 seconds to generate results for one face. In each of these morphs, the left image is the starting input photo and the right image will transform to age 80 to show the automatic aging process. 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 childвЂ™s face as it ages, including vertical stretching, wrinkles and a longer nose.Д«ut this process takes time, and itвЂ™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. 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 childвЂ™s face. Real-life photos of children are difficult to age-progress, partly due to variable lighting, shadows, funny expressions and even milk moustaches. вЂњWhen 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.вЂќ вЂњOur extensive user studies demonstrated age progression results that are so convincing that people canвЂ™t distinguish them from reality,вЂќ said co-author Steven Seitz, a UW 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.ДЄ 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).

The researchers tested their rendered images against those of 82 actual people photographed over a span of years. These changes are then applied to a new childвЂ™s photo to predict how she or he will appear for any subsequent age up to 80. An algorithm then finds correspondences between the averages from each bracket and calculates the average change in facial shape and appearance between ages. More specifically, the software determines the average pixel arrangement from thousands of random Internet photos of faces in different age and gender brackets.

This 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 personвЂ™s face. The shape and appearance of a babyвЂ™s face вЂ“ and variety of expressions вЂ“ often change drastically by adulthood, making it hard to model and predict that change. See more examples of age-progressed photos.