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New insight into how bees see
January 23, 2009New research from Monash University bee researcher Adrian Dyer could lead to improved artificial intelligence systems and computer programs for facial recognition.
Dr Dyer is one of Australia's leading bee experts and his latest research shows that honeybees can learn to recognise human faces even when seen from different viewpoints.
Dr Dyer said the research could be applied in the areas of new technology, particularly the development of imaging systems.
"What we have shown is that the bee brain, which contains less than 1 million neurons, is actually very good at learning to master complex tasks. Computer and imaging technology programmers who are working on solving complex visual recognition tasks using minimal hardware resources will find this research useful," Dr Dyer said.
"Most current artificial intelligence (AI) recognition systems perform poorly at reliably recognising faces from different viewpoints. However the bees have shown they can recognise novel views of rotated faces using a mechanism of interpolating or image averaging previously learnt views."
The findings show that despite the highly constrained neural resources of the insects (their brains are 0.01 per cent the size of the human brain) their ability has evolved so that they're able to process complex visual recognition tasks.
The researchers individually trained different groups of free flying bees with a sugar reward for making correct choices, or alternatively the bees were punished with a bitter tasting solution for incorrect choices. Faces were presented on a vertical screen and bees slowly learnt to fly to the correct target faces. Over the course of a day a bee brain learned a complex task, and then when tested in non-rewarded tests (to totally excluded cues like olfaction) only bees that had experience multiple views (e.g. faces at both 0° and 60°) were able to solve a novel rotational angle of 30°.
Dr Dyer said the discovery helps to answer a fundamental question about how brains solve complex image rotational problems by either image averaging or mentally rotating previously learnt views.
"Bee brains clearly use image interpolation to solve the problem. In other words, bees that had learnt what a particular face looked like from two different viewpoints could then recognise a novel view of this target face. However, bees that had only learnt a single view could not recognise novel views," Dr Dyer said.
The study, performed over two years in Australia and Germany by Dr Dyer with the support of the US Air Force Office of Scientific Research (AFOSR), and Dr Quoc Vuong from Newcastle University UK, was published in the prestigious science journal PLoS ONE.
"The relationships between different components of the object often dramatically change when viewed from different angles but it is amazing to find the bees' brains have evolved clever mechanisms for problem solving which may help develop improved models for AI face recognition systems," Dr Dyer said.
Monash University
"What we have shown is that the bee brain, which contains less than 1 million neurons, is actually very good at learning to master complex tasks. Computer and imaging technology programmers who are working on solving complex visual recognition tasks using minimal hardware resources will find this research useful," Dr Dyer said.
"Most current artificial intelligence (AI) recognition systems perform poorly at reliably recognising faces from different viewpoints. However the bees have shown they can recognise novel views of rotated faces using a mechanism of interpolating or image averaging previously learnt views."
The findings show that despite the highly constrained neural resources of the insects (their brains are 0.01 per cent the size of the human brain) their ability has evolved so that they're able to process complex visual recognition tasks.
The researchers individually trained different groups of free flying bees with a sugar reward for making correct choices, or alternatively the bees were punished with a bitter tasting solution for incorrect choices. Faces were presented on a vertical screen and bees slowly learnt to fly to the correct target faces. Over the course of a day a bee brain learned a complex task, and then when tested in non-rewarded tests (to totally excluded cues like olfaction) only bees that had experience multiple views (e.g. faces at both 0° and 60°) were able to solve a novel rotational angle of 30°.
Dr Dyer said the discovery helps to answer a fundamental question about how brains solve complex image rotational problems by either image averaging or mentally rotating previously learnt views.
"Bee brains clearly use image interpolation to solve the problem. In other words, bees that had learnt what a particular face looked like from two different viewpoints could then recognise a novel view of this target face. However, bees that had only learnt a single view could not recognise novel views," Dr Dyer said.
The study, performed over two years in Australia and Germany by Dr Dyer with the support of the US Air Force Office of Scientific Research (AFOSR), and Dr Quoc Vuong from Newcastle University UK, was published in the prestigious science journal PLoS ONE.
"The relationships between different components of the object often dramatically change when viewed from different angles but it is amazing to find the bees' brains have evolved clever mechanisms for problem solving which may help develop improved models for AI face recognition systems," Dr Dyer said.
Monash University
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Decoding Funny Faces to Detect Disease
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There may be a simple way to address racial bias: Help people improve their ability to distinguish between faces of individuals of a different race.
The Nose Knows: Two Fixation Points Needed for Face Recognition
Many of us are bad at remembering names but we are very quick to point out that at least we never forget a face.
Visual technology enables brain to learn in new ways
New technology at Tufts University's Center for Scientific Visualization is enabling researchers to translate the most abstract, complex scientific concepts into clearer, more precise 3-dimensional images than conventional visualization systems can create.
Stanford researchers developing 3-D camera with 12,616 lenses
The camera you own has one main lens and produces a flat, two-dimensional photograph, whether you hold it in your hand or view it on your computer screen. On the other hand, a camera with two lenses (or two cameras placed apart from each other) can take more interesting 3-D photos.
Technology Would Help Detect Terrorists Before They Strike
Are you a terrorist? Airport screeners, customs agents, police officers and members of the military who silently pose that question to people every day, may soon have much more than intuition to depend on to determine the answer.
Why white light is best for our town centres
White lighting is twice as good at letting you see the face of someone else as that from the yellow light from high pressure sodium lamps commonly used to light our streets, according to new research sponsored by the ESRC and published as part of Social Science week. It can therefore allow the same facial recognition as conventional sodium lighting while cutting energy use by 40 - 45 per cent, says a study led by Professor Tadj Oreszczyn, of the Bartlett School of Graduate Studies, University College London. Professor Oreszczyn said: "People have been putting in sodium light because they think it saves energy. But the truth is, you need more sodium light to be able to see other people's
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