In what could be a major push towards machine learning capabilities, a part of the artificial intelligence (AI) process, scientists in MIT in the US, have created a facial recognition system, that replicated the neurological functions of the human brain, which scientists believe can have a number of business and organizational benefits. Unlike the previous models produced, this one has captured aspects of human brain function, which were earlier missing.
With the newly developed high-grade precise facial recognition system, high security areas could be automated and be turned more secure, as all systems and access would be granted by a visual match of the human face. It could be used for customer services, and other smart devices or robots in time, to make machine-human interaction more accurate and better defined.
Medical research into areas like the brain could be looked into through these machine learning based models, in order to look for medical advancements and potential cures for brain related diseases. At the same time, the internet of Things as it progresses, will depend on Artificial Intelligence a lot more. Since Machine Learning is perhaps the most crucial aspect of the process, especially in the early stages, a lot of facial recognition functions, powered by advance algorithms, could use such a system. Applications could mean smart devices like smart cars, smart public services mechanisms, smart banking mechanisms, etc.
The team of researchers at MIT said that they trained the system to recognize certain human faces, but by first feeding it numerous sample images before. Till now, if these faces were rotated at approximately 45-degrees, the model could recognize the sample faces, but not if they were turned in the left or right directions.
It has also been revealed, that the behavior shown by the system, was not something the team programmed into it, but was a spontaneous process from training the system earlier. The functions were duplicating what the human brain does, while it tends to recognize faces. The scientists have mentioned that, this development is actually an indication of the normal brain’s neurological function and the system. Tomaso Poggio, a professor of Brain and Cognitive Sciences at MIT and also Director of Center of Brains, Minds and Machines, said “This is not a proof that we understand what’s going on. 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.”
According to to Phys, Poggio has been joined by other noted names such as 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.
The new paper that has been authored by Prof. Poggio and the team, looks to explain the entire model and the work they are doing. In fact Poggio described the paper as “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. That means not only what algorithms does the brain use, but what are the circuits in the brain that implement these algorithms.”
Poggio has mainly believed that the brain ought to bring about ;invariant’ representations of faces and other objects. This means that whatever be the distance, orientation in space,or location in the visual field, faces and objects ought to be recognized by the brain.
While this research has a long way to go, researchers believe it represents a step forward in deepening our understanding of the mind, as well as how we could potentially improve machine learning algorithms and artificial intelligence in facial recognition technologies.