Viewpoint dependency debate/ Faces are special

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Madoodles _

Cards (50)

Object recognition is viewpoint invariant
viewpoint independence
never seen those specific pictures before but you can still identify the objects
e.g Bruce’s idea of the structural code in face recognition
AI and viewpoint invarience
Marr
origin of the viewpoint invariance view comes from AI
Marr believed that vision operated in an information-processing manner
A grey level description is an image depicting light intensity changes
Each point (pixel) on the image indicates how much light was received at that point
This is computer vision, so, unlike Bruce’s Pictorial Code in face recognition, it is a computer image made up of pixels
looking at greyscale to find the parts that stick out
Raw Primal Sketch
Find important parts of the image
done by looking for large intensity changes
Description of the boundaries between objects
achieved by several types of blurring
blobs, edge segments and bars can be identified
Proximity
These blobs, edge segments and bars need to be put together
Gestalt laws propose ways in which this might happen
report they see a square made of circles for A
but then saw they see 5 rows/columns of 5 circles for the other 2
square background made of squares and a square made of circles
X in front of a square
similar things go together
People will see the circle as closed on first glance
if it has the chance to carry on it will
could turn anyway
so not good continuation
people will see AOB and COD
most people are asymmetrical
those who have average symmetry faces are usually ranked more attractive
Putting the bits together
Similarity
Closure
Good continuation
Symmetry
The Full Primal Sketch
uses Gestalt principles to determine important regions or lines
contain 3 different levels of depth
closest, medium, furthest away
shown in separate images
3 images on top of each other
2.5D sketch
As the name implies, is somewhere between 2D and 3D
A 2D image is flat and contains no information about depth
A 3D image shows full depth
The 2.5D image contains information about the layout of object surfaces but is still viewpoint-dependent
That is, the appearance can still change with a shift in viewpoint
Object recognition
turning objects in a 2.5D sketch into 3D objects
Marr and Mishihara 1978
proposed they could use structures called primitives to active this
everything is made of cylinders
Recognition by components
Recognition by Components (RBC)
Developed by Biederman (1987)
Objects are also comprised of features
An ‘alphabet’ of 36 geons was proposed
All objects can be constructed from this set of geons
The geons are like to parts of a letters in a feature net
An object is recognised by the geons activating parts of the network
A key concept of RBC is viewpoint-independence
That is, objects can be recognised in any viewpoint
Recognition by Components (RBC)
Developed by Biederman (1987)
Objects are also comprised of features
An ‘alphabet’ of 36 geons was proposed
All objects can be constructed from this set of geons
The geons are like to parts of a letters in a feature net
An object is recognised by the geons activating parts of the network
A key concept of RBC is viewpoint-independence
That is, objects can be recognised in any viewpoint
Biederman 1987
used a flashlight and a more complicated penguin geon
recognition getting worse the more geons you have in the shape
viewpoint invariance
In summary, work by Marr, Marr and Nishihara, and Biederman propose that object recognition is viewpoint-invariant
Objects can be successfully recognised from any viewpoint
There is no cost of changing viewpoint between study and test
This is achieved by a group of set shapes known as geons
viewpoint dependency
Object recognition is dependent upon having a stored mental representation of an object that is the same/similar to a currently presented view
Recognition to a previously unseen view requires generalisation
Already encountered one form of viewpoint-dependence in PSYC412
(Bruce’s idea of the pictorial code in face recognition)
Longmore et al 2008
rotating head
shows profile view then other rotations
gets worse the more you rotate away from the original image
Canonical Views
most people choose 3
canonical viewpoint
shows the most info in one image
Problem for RBS
gets worse with the greater angle of difference
demonstrates viewpoint dependency
Liu 1996
made less mistakes with more symmetry
Reaction times and recognition accuracy is a function of how similar the study and test images are.
There is a steady increase in reaction times and a decrease in recognition accuracy with increasing viewpoint changes
Suggests that object recognition is mediated by mental representations based upon view-dependent descriptors
The viewpoint-dependency debate was at its peak in the 1990s and has now largely subsided
It moved away from the idea of a view-dependent vs. view-invariant mental representation to what is the actual process used for generalisation
Use both Dependent and INvariant processes
Burgund et al 2000
Image of objects tested in the same or different orientation as studied
images presented to the left or right visual field
processed by the right or left hemisphere respectively
mental rotation
Long before the viewpoint-dependency debate some element of viewpoint-dependency was known
Studies on mental rotation seemed to suggest this
Shepard and Meztler 1971
abstract objects (Rubiks snake) presented in pairs
could be same or different
angular difference between 2 objects could vary
Ps task to determine whether the ibjects are the same or different
the time participants took to judge whether two rotated abstract block figures were identical increased with the figures' relative angular disparity
Are there dedicated networks that are innate for recognising faces.
It is difficult to define faces as special as in face recognition faces are special because they convey important information(as stated by bruce and young)
special=dedicated neural networks dedicated to processing specific stimuli
Evidence for faces being special
Some evidence for faces being special is the N170. this is an ERP(event related potential). When people are shown different stimuli the brain processes the information on different levels. It also has negative potential meaning down is good.
FFA specificity:
Fusiform face area(fusiform gyrus) responds largely to faces. kanwisher(1997) believes this is because the FFA was designed to process faces while gauthier believes that it is a general area for expertise(anything you are good at)
Developmental evidence:fantz(1961)
Fantz was interested in what babies could actually see(because of this we know babies have blurry vision) fantz narrowed bars on a stimulus until the baby couldn't see the difference and no longer paid attention. He then decided to see how long babies looked at different stimuli for lengths of time
black bar= under 3 months white bar= over 3 months
This shows that babies looked at the face the most at any age, even though the top 3 stimuli all had the same contrast(black and white) the face was still on top, suggesting that they favoured the face.
Johnson et al:1991
24 infants less than an hour old
camera at the top records the babies reactions, the mother holds a paddle with either a face(or part of a face/shapes of a face) or nothing on and the mother would move the paddle over the baby to see if the baby reacted.
0= the baby looking directly up
The face lead to the baby moving its head and eyes more than anything else. This suggests that babies are born with what faces look like and it is an innate process.
This then leads to 2 systems being presented by Johnson and Morton(1991)- Conspec and Conlearn
CONSPEC:
An innate subcortical system
It drives an infants attention to faces to aid with survival(drawn to other people)
It is necessary to drive the shaping of face processing(it understands face shape but are not able to recognise a face yet)
All this suggests that there are innate schematics for face processing
However, there is alot of critics
CONLEARN:
Cortical, face specific mechanism that takes over from CONSPEC at 1-2 months
It is more flexible and therefore more accepted
When there are visual issues from birth, face learning is impaired as it is not able to act
One of the problems with CONSPEC was shown in a study by simion et al(2003)
Infants were shown top heavy or bottom heavy
stimuli.
It was found that infants preferred the top heavy pattern because there is the most contrast there and there is the most contrast at the ‘top part’ of the human face
indicating that it is not the face they are focusing on but the contrast
Another issue with CONSPEC was shown by pascalis(1994),
they reported that 3-4 day old infants will habituate to a strangers face(they would ignore it),
their argument that CONSPEC is supposed to draw the infant to the face and CONLEARN is not developed yet so the infant cannot have learned the face.
But the infant was shown the face alot so it might not be interesting anymore
Perceptual narrowing:
Perceptual narrowing(developed by nelson, 2001) has lots in common with CONLEARN.
This is the gradual learning of human faces, even if you don't recognise human faces, you can tell them apart.
As you start to develop you are better able to define the difference of different faces(pascalis et al 2002).
Kelly et al(2005)
showed that 3 month old infants prefer faces of the area that they grow up race to faces of other races as that is the race that they see more often, but this preference was not present at birth.
Meaning the most commonly seen race is prefered by the infant.
Neuropsychology:
Neuropsychology studies people with brain injuries and tests what they can do vs what they cant do and then compare that to the general public to see what damage does what.
Visual agnosia = when presented with an object he is unable to say what the object is
Sheep recognition!!!!!!!
Sheep farmer who cant recognise his wife but he can recognise his sheep(in rather face like ways)
he knew they were different
WJ the sheep farmer
mcneill and warringon (1993)
WJ had a stroke and became a sheep farmer
recognise the identity of his sheep but not his friends/family.
Results:
he recognised his sheep and other sheep.
evidences the fact that there must be a face-specific impairment as face recognition is down to chance while sheep recognition is high
Mr W prosopagnosia
Developed face recognition problems as a result of heart problems.
He had a number of abilities-
Normal IQ
Could identify faces from other objects
Could copy faces
Could state the sex of the face
Could state the expression of the face
Could match unfamiliar faces
He could recognise people from their voice(he hasn't forgotten about people)
However he could not recognise the faces(he couldn't identify himself or doctors or friends) therefore it is face recognition, not everything facial, just recognising them.
Propopagnosia - inability to recognise faces