Nav: Home

New algorithm could explain human face recognition

December 01, 2016

MIT researchers and their colleagues have developed a new computational model of the human brain's face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.

The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face's degree of rotation -- say, 45 degrees from center -- but not the direction -- left or right.

This property wasn't built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism. The researchers consider this an indication that their system and the brain are doing something similar.

"This is not a proof that we understand what's going on," says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM), a multi-institution research consortium funded by the National Science Foundation and headquartered at MIT. "Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But I think it's strong evidence that we are on the right track."

Indeed, the researchers' new paper includes a mathematical proof that the particular type of machine-learning system they use, which was intended to offer what Poggio calls a "biologically plausible" model of the nervous system, will inevitably yield intermediary representations that are indifferent to angle of rotation.

Poggio, who is also a primary investigator at MIT's McGovern Institute for Brain Research, is the senior author on a paper describing the new work, which appeared today in the journal Computational Biology. He's joined on the paper by several other members of both the CBMM and the McGovern Institute: first author Joel Leibo, a researcher at Google DeepMind, who earned his PhD in brain and cognitive sciences from MIT with Poggio as his advisor; Qianli Liao, an MIT graduate student in electrical engineering and computer science; Fabio Anselmi, a postdoc in the IIT@MIT Laboratory for Computational and Statistical Learning, a joint venture of MIT and the Italian Institute of Technology; and Winrich Freiwald, an associate professor at the Rockefeller University.

Emergent properties

The new paper is "a nice illustration of what we want to do in [CBMM], which is this integration of machine learning and computer science on one hand, neurophysiology on the other, and aspects of human behavior," Poggio says. "That means not only what algorithms does the brain use, but what are the circuits in the brain that implement these algorithms."

Poggio has long believed that the brain must produce "invariant" representations of faces and other objects, meaning representations that are indifferent to objects' orientation in space, their distance from the viewer, or their location in the visual field. Magnetic resonance scans of human and monkey brains suggested as much, but in 2010, Freiwald published a study describing the neuroanatomy of macaque monkeys' face-recognition mechanism in much greater detail.

Freiwald showed that information from the monkey's optic nerves passes through a series of brain locations, each of which is less sensitive to face orientation than the last. Neurons in the first region fire only in response to particular face orientations; neurons in the final region fire regardless of the face's orientation -- an invariant representation.

But neurons in an intermediate region appear to be "mirror symmetric": That is, they're sensitive to the angle of face rotation without respect to direction. In the first region, one cluster of neurons will fire if a face is rotated 45 degrees to the left, and a different cluster will fire if it's rotated 45 degrees to the right. In the final region, the same cluster of neurons will fire whether the face is rotated 30 degrees, 45 degrees, 90 degrees, or anywhere in-between. But in the intermediate region, a particular cluster of neurons will fire if the face is rotated by 45 degrees in either direction, another if it's rotated 30 degrees, and so on.

This is the behavior that the researchers' machine-learning system reproduced. "It was not a model that was trying to explain mirror symmetry," Poggio says. "This model was trying to explain invariance, and in the process, there is this other property that pops out."

Neural training

The researchers' machine-learning system is a neural network, so called because it roughly approximates the architecture of the human brain. A neural network consists of very simple processing units, arranged into layers, that are densely connected to the processing units -- or nodes -- in the layers above and below. Data are fed into the bottom layer of the network, which processes them in some way and feeds them to the next layer, and so on. During training, the output of the top layer is correlated with some classification criterion -- say, correctly determining whether a given image depicts a particular person.

In earlier work, Poggio's group had trained neural networks to produce invariant representations by, essentially, memorizing a representative set of orientations for just a handful of faces, which Poggio calls "templates." When the network was presented with a new face, it would measure its difference from these templates. That difference would be smallest for the templates whose orientations were the same as that of the new face, and the output of their associated nodes would end up dominating the information signal by the time it reached the top layer. The measured difference between the new face and the stored faces gives the new face a kind of identifying signature.

In experiments, this approach produced invariant representations: A face's signature turned out to be roughly the same no matter its orientation. But the mechanism -- memorizing templates -- was not, Poggio says, biologically plausible.

So instead, the new network uses a variation on Hebb's rule, which is often described in the neurological literature as "neurons that fire together wire together." That means that during training, as the weights of the connections between nodes are being adjusted to produce more accurate outputs, nodes that react in concert to particular stimuli end up contributing more to the final output than nodes that react independently (or not at all).

This approach, too, ended up yielding invariant representations. But the middle layers of the network also duplicated the mirror-symmetric responses of the intermediate visual-processing regions of the primate brain.
-end-
Additional background

ARCHIVE: Machines that learn like people

ARCHIVE: More-flexible machine learning

ARCHIVE: Artificial-intelligence research revives its old ambitionsARCHIVE: How the brain recognizes objects

Massachusetts Institute of Technology

Related Neurons Articles:

How do we get so many different types of neurons in our brain?
SMU (Southern Methodist University) researchers have discovered another layer of complexity in gene expression, which could help explain how we're able to have so many billions of neurons in our brain.
These neurons affect how much you do, or don't, want to eat
University of Arizona researchers have identified a network of neurons that coordinate with other brain regions to influence eating behaviors.
Mood neurons mature during adolescence
Researchers have discovered a mysterious group of neurons in the amygdala -- a key center for emotional processing in the brain -- that stay in an immature, prenatal developmental state throughout childhood.
Astrocytes protect neurons from toxic buildup
Neurons off-load toxic by-products to astrocytes, which process and recycle them.
Connecting neurons in the brain
Leuven researchers uncover new mechanisms of brain development that determine when, where and how strongly distinct brain cells interconnect.
The salt-craving neurons
Pass the potato chips, please! New research discovers neural circuits that regulate craving and satiation for salty tastes.
When neurons are out of shape, antidepressants may not work
Selective serotonin reuptake inhibitors (SSRIs) are the most commonly prescribed medication for major depressive disorder (MDD), yet scientists still do not understand why the treatment does not work in nearly thirty percent of patients with MDD.
Losing neurons can sometimes not be that bad
Current thinking about Alzheimer's disease is that neuronal cell death in the brain is to blame for the cognitive havoc caused by the disease.
Neurons that fire together, don't always wire together
As the adage goes 'neurons that fire together, wire together,' but a new paper published today in Neuron demonstrates that, in addition to response similarity, projection target also constrains local connectivity.
Scientists accidentally reprogram mature mouse GABA neurons into dopaminergic-like neurons
Attempting to make dopamine-producing neurons out of glial cells in mouse brains, a group of researchers instead converted mature inhibitory neurons into dopaminergic cells.
More Neurons News and Neurons Current Events

Top Science Podcasts

We have hand picked the top science podcasts of 2019.
Now Playing: TED Radio Hour

In & Out Of Love
We think of love as a mysterious, unknowable force. Something that happens to us. But what if we could control it? This hour, TED speakers on whether we can decide to fall in — and out of — love. Guests include writer Mandy Len Catron, biological anthropologist Helen Fisher, musician Dessa, One Love CEO Katie Hood, and psychologist Guy Winch.
Now Playing: Science for the People

#543 Give a Nerd a Gift
Yup, you guessed it... it's Science for the People's annual holiday episode that helps you figure out what sciency books and gifts to get that special nerd on your list. Or maybe you're looking to build up your reading list for the holiday break and a geeky Christmas sweater to wear to an upcoming party. Returning are pop-science power-readers John Dupuis and Joanne Manaster to dish on the best science books they read this past year. And Rachelle Saunders and Bethany Brookshire squee in delight over some truly delightful science-themed non-book objects for those whose bookshelves are already full. Since...
Now Playing: Radiolab

An Announcement from Radiolab