Prosopagnosia - Face Blidness

Prosopagnosia is a disorder of face perception where the ability to recognize faces is impaired, while the ability to recognize other objects may be relatively intact.

The term originally referred to a condition following acute brain damage, but a congenital form of the disorder has been proposed, which may be inherited by about 2.5% of the population.

The specific brain area usually associated with prosopagnosia is the fusiform gyrus.

Few successful therapies have so far been developed for affected people, although individuals often learn to use 'piecemeal' or 'feature by feature' recognition strategies.

This may involve secondary clues such as clothing, gait, hair colour, body shape, and voice. Because the face seems to function as an important identifying feature in memory, it can also be difficult for people with this condition to keep track of information about people, and socialize normally with others.

Some also use the term prosophenosia, which refers to the inability to recognize faces following extensive damage of both occipital and temporal lobes.

Children with Prosopagnosia
Developmental prosopagnosia can be a difficult thing for a child to both understand and cope with. Many adults with developmental prosopagnosia report for a long time they had no idea that they had a deficit in face processing, unaware that others could distinguish people solely on facial differences.

Children with prosopagnosia can be hard to find. They may just appear to be very shy or slightly odd due to their inabilities to recognise faces.

Children with prosopagnosia may have a hard time making friends, as they may not recognize their classmates. They often make friends with children with other distinguishing features.

Children with prosopagnosia may also have difficulties following the plots of television shows and movies, as they have trouble recognizing the different characters.

They tend to gravitate towards cartoons, where the characters always wear the same thing and have other distinguishing features.

Prosopagnosiac children may also have a hard time telling family members apart or recognizing people out of context (i.e. the teacher in a grocery store).

Additionally, those children with prosopagnosia can have a difficult time with the public school system, as many school professionals are not well versed in prosopagnosia, if they are aware of the disorder at all.

Resources
Resources to help parents and professionals cope with prosopagnosia in children are also being developed, such as Understanding Facial Recognition Disorders in Children by Nancy L. Mindick

Oliver Sacks, famous neuroscientist, author of many books including The Man Who Mistook His Wife for a Hat; although he knew what prosopagnosia was and had studied it, he did not realise he had it until people became shocked that he confused one of his brothers with the other and then, discussing it with family members, learned that a number of them had similar difficulties with face.

Dame Jane Goodall, British primatologist, ethologist, and anthropologist, best known for her 45-year study of social and family interactions of wild chimpanzees.

Face Recognition

There is a new "face" in face recognition and this could be in the form of a dot. This is what Associate Professor Ajmal Mian from the University of Western Australia is working on.

Associate Professor Ajmal Mian is an expert in face recognition and has been in the field for 8 years. His new research study is focused on utilizing satellite technology in order to identify facial features that are located under the skin.

There is also a possibility that people who utilized cosmetic surgery to alter their face could be recognized through the use of this technology.

He further explained his research stating that "multi-spectral imaging can be used to measure light reflected off a face at hundreds of discrete wavelengths in the visible spectrum and beyond".

"Recognition based on sets of facial images from surveillance cameras, YouTube videos, Google Images or personal photo albums is more accurate because they contain more information," he said.

Face recognition technology is being used increasingly for computer log-ons, identity checks and surveillance, and is a boom industry around the world.

"It can be used in any kind of machine such as mobile phones, computers and robots. It's the most user-friendly way to authenticate someone and is now so sophisticated that machines can identify a face no matter what the expression.

"Humans are very good at finding a familiar face in a crowd but less able to identify someone they may have seen only once. This is where machines outperform people because they can memorise images and never tire of matching them to faces in a crowd."

Associate Professor Ajmal said face recognition technology was better than fingerprinting because it didn't require special equipment or an expert to verify the results.

Also, any part of a face could be used, and many images of a person's face - including different expressions and poses - could be merged to make a composite image which was more meaningful to the machine.

"Humans can recognise a person regardless of whether they're laughing, frowning, crying or sleeping. Machines may soon be able to do the same."

Associate Professor Mian is the only West Australian to have won the Australasian Distinguished Dissertation Award from CORE (The Computing Research and Education Association of Australasia).

He has won two prestigious national fellowships: the Australian Postdoctoral Fellowship and the Australian Research Fellowship, and has written more than 50 high-impact papers including more than 30 as first author.

Speaking and Understanding Speech Share the Same Parts of the Brain

The brain has two big tasks related to speech: making it and understanding it.

Psychologists and others who study the brain have debated whether these are really two separate tasks or whether they both use the same regions of the brain.

Now, a new study, published in the August issue of Psychological Science, a journal of the Association for Psychological Science, finds that speaking and understanding speech share the same parts of the brain, with one difference: we don't need the brain regions that control the movements of lips, teeth, and so on to understand speech.

Most studies of how speech works in the brain focuses on comprehension. That's mostly because it's easier to image the brains of people who are listening quietly; talking makes the head move, which is a problem when you're measuring the brain.

But now, the Donders Institute at the Radboud University Nijmegen, where the study was conducted, has developed technology that allows recording from a moving brain.

Laura Menenti, a Postdoctoral Research Associate at the University of Glasgow, co-wrote the paper along with Peter Hagoort of Radboud University Nijmegen and the Max Planck Institute for Psycholinguistics, Sarah Gierhan and Katrien Segaert.

Menenti was initially interested in how the brain produces grammatical sentences and wanted to track the process of producing a sentence in its entirety; looking not only at its grammatical structure but also at its meaning.

"What made this particularly exciting to us was that no one had managed to perform such a study before, meaning that we could explore an almost completely new topic," says Menenti.

The authors used functional MRI technology to measure brain activity in people who were either listening to sentences or speaking sentences.

The other problem with measuring brain activity in people who are speaking is that you have to get them to say the right kind of sentence.

The authors accomplished this with a picture of an action -- a man strangling a woman, say -- with one person coloured green and one coloured red to indicate their order in the sentence.

This prompted people to say either "The man is strangling the woman" or "The woman is strangled by the man." (The experiments were all carried out in Dutch.)

From this, the researchers were able to tell where in the brain three different speech tasks (computing meaning, coming up with the words, and building a grammatical sentence) -- were taking place.

They found that the same areas were activated for each of these tasks in people who were speaking and people who were listening to sentences.

However, although some studies have suggested that while people are listening to speech, they silently articulate the words in order to understand them, the authors found no involvement of motor regions when people were listening.

According to Menenti, though the study was largely designed to answer a specific theoretical question, it also points towards some useful avenues for treatment of people with language-related problems.

It suggests that while it sometimes seems that people with comprehension problems may have intact production, and vice versa, this may not necessarily be the case. According to Menenti, "Our data suggest that these problems would be expected to always at least partly coincide.

On the other, our data confirm the idea that many different processes in the language system, such as understanding meaning or grammar, can at least partly, be damaged independently of each other."

Automatic Speech Recognition Software; Effectiveness

MOST of us talk to our computers, if only to curse them when a glitch destroys hours of work. Sadly the computer doesn't usually listen, but new kinds of software are being developed that make conversing with a computer rather more productive.

The longest established of these is automatic speech recognition (ASR), the technology that converts the spoken word to text. More recently it has been joined by subtler techniques that go beyond what you say, and analyse how you say it. Between them they could help us communicate more effectively in situations where face-to-face conversation is not possible.

ASR has come a long way since 1964, when visitors to the World's Fair in New York were wowed by a device called the IBM Shoebox, which performed simple arithmetic calculations in response to voice commands. Yet people's perceptions of the usefulness of ASR have, if anything, diminished.

"State-of-the-art ASR has an error rate of 30 to 35 per cent," says Simon Tucker at the University of Sheffield, UK, "and that's just very annoying." Its shortcomings are highlighted by the plethora of web pages poking fun at some of the mistakes made by Google Voice, which turns voicemail messages into text.

What's more, even when ASR gets it right the results can be unsatisfactory, as simply transcribing what someone says often makes for awkward reading. People's speech can be peppered with repetition, or sentences that just tail off.

"Even if you had perfect transcription of the words, it's often the case that you still couldn't tell what was going on," says Alex Pentland, who directs the Human Dynamics Lab at the Massachusetts Institute of Technology. "People's language use is very indirect and idiomatic," he points out.

Despite these limitations, ASR has its uses, says Tucker. With colleagues at Sheffield and Steve Whittaker at IBM Research in Almaden, California, he has developed a system called Catchup, designed to summarise in almost real time what has been said at a business meeting so the latecomers can... well, catch up with what they missed. Catchup is able to identify the important words and phrases in an ASR transcript and edit out the unimportant ones.

It does so by using the frequency with which a word appears as an indicator of its importance, having first ruled out a "stop list" of very common words. It leaves the text surrounding the important words in place to put them in context, and removes the rest.

A key feature of Catchup is that it then presents the result in audio form, so the latecomer hears a spoken summary rather than having to plough through a transcript. "It provides a much better user experience," says Tucker.

Read the full article here .....

Privacy and the new CCTV surveillance technology

Privacy protected with the new CCTV surveillance technology. (Image: 3VR)

Most people are unhappy about being filmed by CCTV, in case the footage is used against them in some unlikely way. Now 3VR, a surveillance technology company has come up with a method of scrambling the images of anyone in CCTV film who is not a suspect.

3VR, based in San Francisco, says it should reassure members of the public who do not wish to be identifiable to police or lawyers, or even TV crime-stopper shows.

Face-recognition Algorithms

The technology uses 3VR's recently patented face-recognition algorithms to home in on known faces in crowds. An image-scrambling algorithm then blurs the faces and bodies of those who are not of interest and encrypts the blur pattern. So, no one but the operator of the technology can unscramble it.

An image-scrambling algorithm blurs the faces of those in the footage who are not of interest

Monitor Known Felons

"This allows you to search for suspects, known felons and people on watch lists, but without capturing massive databases of apparently innocent people," says Stephen Russell, 3VR's chairman. The company aims to supply the equipment to banks and retail chains so they can analyse CCTV footage for known suspects. Criminals who install card skimmers on ATMs are a likely target.

Open to Abuse

The idea is unlikely to satisfy all privacy advocates, since scrambled footage is still open to abuse. "A safer approach is to record images, only when machine analysis detects something suspicious and that way there is no recording of anything, for 99.99 per cent of the time," says Mike Lynch of data-analysis company Autonomy.