Artificial intelligence estimates peoples' ages

Date:
June 15, 2020
Source:
Ruhr-University Bochum
Summary:
Wrinkles, furrows, spots: a person's aging process is accompanied
by tell-tale signs on their face. Researchers have developed an
algorithm that interprets these features very reliably.



FULL STORY ========================================================================== Wrinkles, furrows, spots: a person's aging process is accompanied
by tell-tale signs on their face. Researchers from the Institute for
Neural Computation at Ruhr-Universita"t Bochum (RUB) have developed
an algorithm that interprets these features very reliably. It makes it
possible to estimate the age and ethnicity of people so accurately that
it catapulted RUB researchers to the top of the league table worldwide
for a while. The RUB team published its report in the journal Machine
Learning from May 2020.


==========================================================================
The system has learned to estimate "We're not quite sure what features
our algorithm is looking for," says Professor Laurenz Wiskott from the Institute for Neural Computation. This is because the system has learned
to assess faces. The successful algorithm developed by the Bochum-based researchers is a hierarchical neural network with eleven levels. As input
data, the researchers fed it with several thousand photos of faces of
different ages. The age was known in each case.

"Traditionally, the images are the input data and the correct age is the
target fed into the system, which then tries to optimise the intermediate
steps to assess the required age," explains lead author Alberto Escalante.

However, the researchers from Bochum chose a different approach. They
input the many photos of faces sorted by age. The system then ignores
the features that vary from one picture to the next and takes solely
those features into consideration that change slowly. "Think of it
as a film compiled of thousands of photos of faces," explains Laurenz
Wiskott. "The system fades out all features that keep changing from one
face to the next, such as eye colour, the size of the mouth, the length
of the nose. Rather, it focuses on features that slowly change across
all faces." For example, the number of wrinkles slowly but steadily
increases in all faces. When estimating the age of the people pictured
in the photos, the algorithm is only just under three and a half years
off on average. This means that it outperforms even humans, who are real experts in face recognition and interpretation.

The system also recognises ethnic origins The slowness principle also
enabled it to reliably identify ethnic origin. The images were presented
to the system sorted not only by age, but also by ethnicity. Accordingly,
the features characteristic of an ethnic group didn't change quickly
from image to image; rather, they changed slowly, albeit by leaps and
bounds. The algorithm estimated the correct ethnic origin of the people
in the photos with a probability of over 99 percent, even though the
average brightness of the images was standardised and, consequently,
skin colour wasn't a significant marker for recognition.


========================================================================== Story Source: Materials provided by Ruhr-University_Bochum. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Alberto N. Escalante-B., Laurenz Wiskott. Improved graph-based SFA:
information preservation complements the slowness principle. Machine
Learning, 2019; 109 (5): 999 DOI: 10.1007/s10994-019-05860-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200615100945.htm

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