real time gesture recognition of human hand
DESCRIPTION
TRANSCRIPT
Overview:
• What are gestures?
• What can gestures be used for?
• How to find a hand in an image?
• How to recognise its shape?
• How to recognise its motion?
• How to find its position in 3D space?
What is Gesture?
A movement of a limb or the body as an expression of thought or feeling.
--Oxford Concise Dictionary 1995
Mood, emotion
• Mood and emotion are expressed by body language
• Facial expressions
• Tone of voice
• Allows computers to interact with human beings in a more natural way
Human Computer Interface using Gesture
• Replace mouse and keyboard
• Pointing gestures
• Navigate in a virtual environment
• Pick up and manipulate virtual objects
• Interact with a 3D world
• No physical contact with computer
• Communicate at a distance
Public Display Screens
• Information display screens
• Supermarkets
• Post Offices, Banks
• Allows control without having to touch the device
Sign Language
• 5000 gestures in vocabulary• each gesture consists of a hand shape, a hand
motion and a location in 3D space• facial expressions are important• full grammar and syntax• each country has its own Sign language• Irish Sign Language is different from British Sign
Language or American Sign Language
A C F
Datagloves
Datagloves
• Datagloves provide very accurate measurements of hand-shape
• But are cumbersome to wear
• Expensive
• Connected by wires- restricts freedom of movement
Datagloves - the future
• Will get lighter and more flexible
• Will get cheaper ~ $100
• Wireless?
• Our vision-based system
Wireless & Flexible No specialised hardware
Single Camera Real-time
Coloured Gloves
• User must wear coloured gloves
• Very cheap
• Easy to put on
• BUT get dirty
• Eventually we wish to use natural skin
Colour Segment
NoiseRemoval
Scale by Area
32
32
Feature Space
Each point represents a different imageClusters of points represent different hand-shapesDistance between points depends on how similar the images are
A continuous gesture creates a trajectory in feature space
We can project a new image onto the trajectory
Multiple sub-spaces
Global space
Gesture 1Gesture 2
Classifying a new unknown image
3D spatial position of hand
Subspaces and trajectories calculated with hand at origin
We know the image co-ordinates and the area of the hand in the original image
We can calculate depth and xy-position
camera
x
y
A B C Y
Yes/No? Yes/No?Yes/No?
Yes/No?
Hierarchical Search
• We need to search thousands of images
• How to do this efficiently?
• We need to use a “coarse-to-fine”search strategy
Original image Blurring Factor = 1
Blurring Factor = 2Blurring Factor = 3
Factor = 3.0
Factor = 2.0
Factor = 1.0
• Multi-scale Hierarchy
• Hidden Markov Model ( HMM )--- time sequence of images modeling
Motion Recognition
HMM1 (Hello)
HMM2 (Good)
HMM3(Bad)
HMM4 (House)
P(f |HMM1)f
P(f |HMM2)
Prediction and Tracking
•Given previous frames we can predict what will happen next
•Speeds up search.
•occlusions -
Co-articulation
In fluent dialogue signs are modified by preceding and following signs
intermediate forms
A
B
Future Work:
• Occlusions (Atid)
• Grammars in Irish Sign Language.
--- Sentence Recognition
• Body Language.
Face Recognition
A noisy environment
Errors
Model-based Recognition
Pose-tracking
Facial Expressions
Anger Fear Disgust Happy Sad Surprise
Human Body Tracking
Face Recognition
• Summary– Single pose– Multiple pose– Principal components analysis– Model-based recognition– Neural Networks
Single Pose
• Standard head-and-shoulders view with uniform background
• Easy to find face within image
Aligning Images
Alignment– Faces in the training set must be aligned with
each other to remove the effects of translation, scale, rotation etc.
– It is easy to find the position of the eyes and mouth and then shift and resize images so that are aligned with each other
Nearest Neighbour
• Once the images have been aligned you can simply search for the member of the training set which is nearest to the test image.
• There are a number of measures of distance including Euclidean distance, and the cross-correlation
Principal Components
• PCA reduces the number of dimensions and so the memory requirement is much reduced.
• The search time is also reduced
Two ways to apply PCA (1)
• We could apply PCA to the whole training set.
• Then each face is represented by a point in the PC space
• We could then apply nearest neighbour to these points
Two ways to apply PCA (2)
• Alternatively we could apply PCA to the set of faces belonging to each person in the training set
• Each class (person) is then reprented by a different ellipsoid and Mahalanobis distance can be used to classify a new unknown face
• You need a lot of images of each person to do this
Problems with PCA
• The same person may sometimes appear differently due to– Beards, moustaches– Glasses,– Makeup
• These have to be represented by different ellipsoids
-------(2)--------------(3)--------------(4)-------
-------(5)--------------(6)--------------(7)-------
-------(8)--------------(9)--------------(10)-------
Problems with PCA
• Facial expressions– Differing facial expressions
• Opening and closing the mouth• Raised eyebrows• Widening the eyes• Smiling, frowing etc,
• These mean that the class is no longer ellipsoidal and must be represented by a manifold
Facial Expressions
• There are six types of facial expression• We could use PCA on the eyes and mouth – so we
could have eigeneyes and eigenmouths
Anger Fear Disgust Happy Sad Surprise
Multiple Poses
• Heads must now be aligned in 3D world space
• Classes now form trajectories in feature space
• It becomes difficult to recognise faces because the variation due to pose is greater than the variation between people
Model-based Recognition
• We can fit a model directly to the face image
• Model consists of a mesh which is matched to facial features such as the eyes, nose, mouth and edges of the face.
• We use PCA to describe the parameters of the model rather than the pixels.
Model-based Recognition
• The model copes better with multiple poses and changes in facial expression.
Coarse Classification
• Fingerprints can be divided into 6 basic classes (some systems use other classes)– Arch– Tented Arch– Whorl– Right loop– Left Loop– Double Loop
Orientation Field
• The orientation field gives the ridge direction at each point in the image
Identifying Core and Delta
• The orientation field can be used to identify the core and delta in an image
PCA applied to fingerprints
• We can align different images so that their cores line up
• We can then apply PCA to the orientation fields just as we did to face images
• We can project a new unknown image into the PC space and find the nearest matches in the training set.
Accurate Matching
• Orientation fields and PCA are not good enough to give an accurate match
• They can reduce the number of possible candidates
• We can then apply a more accurate but time-consuming technique to the remaining candidate images
Minutiae Matching
• Minutiae are fine details of the ridges in the fingerprint image such as– Ridge terminations,– Crossovers– Bifurcations etc.
• The pattern of minutiae is unique to each individual person
Minutiae Types
• Different systems define different types of minutiae
• The most common are terminations (ridge endings) and bifurcations(forks)
Binarisation and Thinning
• Binarisation– Every pixel is set to
either 0 or 1
• Thinning– Lines are thinned to a
width of 1 pixel
Identifying minutiae
• Each black pixel in the image is classified using its “crossing number”.
• The crossing number of pixel p is defined as:
cn(p)= Half the sum of the differences between adjacent pixels in the 8-
neighbourhood of p
Identifying Minutiae
• If the crossing number cn is equal to 2 then the pixel is not a minutia but a normal intra-ridge pixel
• If the crossing number is not equal to 2 then the pixel is some kind of minutia
Removing false-minutiae
Minutiae on a real image
• The position of the minutiae is marked
• The direction of the ridge at each minutia is shown as a short line
Matching minutiae between fingerprints
• We now have to compare minutiae between two different fingerprints to see if they came from the same person
• Remember the two images may have been rotated, translated or distorted with respect to one another
• We have to find the combination of rotations, translations and distortions which gives the largest number of matched pairs of minutiae
Two different impressions of the same finger
Matched pairs of minutiae
• Each pair must match position, direction and type
Hough Transform
• Discretise the range of values for translation, rotation and distortion
• Set up an accumulator matrix A in which each element represents a different combination of translation, rotation and distortion
• For each possible pair of minutiae calculate the best values of translation, rotation and distortion which makes them match
• Increase the corresponding element of A by 1 • At the end of the process the element of A with the largest
value represents the best combination
An alternative to minutiae-matching
• FingerCodes– 1. Centre image on core– 2. Divide image into circular zones– 3. Pass each zone through a set of 8 Gabor
filters (more about this next week)– 4. Compare the results using Euclidean distance
Iris Recognition
John Daugman
• There is only one iris recognition algorithm in use
• The algorithm was developed mainly by John Daugman, PhD, OBE
• www.CL.cam.ac.uk/users/jgd1000/
• It is owned by the company Iridian Technologies
Advantages of Iris Recognition
• Irises do not change with age – unlike faces
• Irises do not suffer from scratches, abrasions, grease or dirt – unlike fingerprints
• Irises do not suffer from distortion – unlike fingerprints
Finding the Iris in the Image
• It is easy to find the circular boundaries of the iris
Masking
• The boundaries of the eyelids can be found
• Eyelashes and specularities (reflections) can be found
• These areas can be masked out
Gabor Wavelets
Gabor Wavelets
• Gabor Wavelets filter out structures at different scales and orientations
• For each scale and orientation there is a pair of odd and even wavelets
• A scalar product is carried out between the wavelet and the image (just as in the Discrete Fourier Transform)
• The result is a complex number
Phase Demodulation
• The complex number is converted to 2 bits
• The modulus is thrown away because it is sensitive to illumination intensity
• The phase is converted to 2 bits depending on which quadrant it is in
IrisCodes
• This process is carried out at a number of points throughout the image.
• The result is 2048 bits which describe each iris uniquely• Two codes from different irises can be compared by
finding the number of bits different between them – this is called the Hamming distance
• This is equivalent to computing an XOR between the two codes. This can be done very quickly
• To allow for rotation of the iris images the codes can be shifted with respect to each other and the minimum Hamming distance found
Hamming Distance
bits of no. Total
different bits of No.HD
Binomial Distribution
• If two codes come from different irises the different bits will be random
• The number of different bits will obey a binomial distribution with mean 0.5
Identification
• If two codes come from the same iris the differences will no longer be random
• The Hamming distance will be less than expected than if the differences were random
• If the Hamming distance is < 0.33 the chances of the two codes coming from different irises is 1 in 2.9 million
• So far it has been tried out on 2.3 million people without a single error
More Advantages of IrisCodes
• IrisCodes are extremely accurate
• Matching is very fast compared to fingerprints or faces
• Memory requirments are very low – only 2048 bits per iris
Disadvantages of the Iris for Identification
•Small target (1 cm) to acquire from a distance (1 m) •Moving target ...within another... on yet another •Located behind a curved, wet, reflecting surface •Obscured by eyelashes, lenses, reflections •Partially occluded by eyelids, often drooping •Deforms non-elastically as pupil changes size •Illumination should not be visible or bright •Some negative (Orwellian) connotations
Fake Iris Attacks
Fake Iris Fourier Spectrum
• Due to the dot matrix grid the Fourier Spectrum of the fake iris has 4 extra points
