Object Recognition in the
Dynamic Link Architecture
Yang Ran
CMPS 828J
Outline
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Background and Introduction
System Overview
General algorithm in details
Implementations of the algorithm
Experiment results
Further readings and conclusion
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Background
1. Problem: To recognize human faces
from single images our of a large
gallery.
2. Challenges: Distortions in terms of
position, size , expression, and pose
3. Existed methods:
 Appearance Based v.s. Shape based
 2D vs. 3D
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Background: Notations
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Image: face image
Model: face gallery
Graph: a concise face description
Jet: A local description of the
distribution based on the Gabor
transform
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System Overview
1. Faces are
represented
as
rectangular
graphs by
layers of
neurons
2. Each neuron
represents a
node and has
a jet attached
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Assumptions
The image domain and the model domain
are bi-directionally connected by dynamic
links.
These connections are plastic on a fast time
scale, changing radically during a single
recognition event
The strength of a connection between any
two nodes in the image and a model is
controlled by the jet similarity between them,
which roughly corresponds to the number of
features that are common to the two nodes
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Key Factors
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Basic representation is the labeled graph
formed by edges and vertices bundled in
jets
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Edge Labels: distance information
Vertex/Node Labels: wavelet responses
Graph should be able to deform to adapt
to the variations of human faces
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Preprocessing by Gabor Wavelets
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Gabor Wavelets are biological motivated
convolution kernels in the shape of plane
waves restricted by Gaussian envelope
function
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More for Gabor
Why use it?
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A good approximation to the sensitivity profiles
of neurons found in visual cortex of higher
vertebrates
Cells come in pair with even and odd symmetry
like the real and imagery part of Gabor Filter
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Jets Generation
1. The set of convolution coefficients for
kernels and frequencies at one image
pixel is called a jet
2. Describes a small patch of gray values
around a given pixel
3. Sample W at five logarithmically spaced f
levels and eight directions by u, v
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Jets Generation-cnt’l
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The magnitude of (WI) (kuv, x) form a
feature vector located at x, which will be
referred to as a jet
Evaluate the similarity by Elastic Graph
Matching:
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Edge Labels
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Derived from neuron version, edges
encodes neighborhood relationships
Presents the topology of the vertices
Define
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Quadratic comparison function
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Example
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Graph representation of a face
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Elastic Graph Matching
Elastic matching of a model graph M to a
target graph I amounts to a search for a set of
vertex positions which simultaneously
optimizes the matching of vertex labels and
edge labels according to:
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Elastic Graph Matching-cnt’l
A heuristic algorism is seek to close the optimum
within a reasonable time
Step 1: find approximate face position so that
the image can be scaled and cut to standard
size
Step 2: Extract graph from target face image
Step 3: Match with cost function
Refine position and size with λ = infinity
Local distortion
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Experiments
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Data Base
Technical Aspects
Results
Conclusions
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Data Base
As a face data base we used galleries of 111
different persons. Of most persons there is one
neutral frontal view, one frontal view of different
facial expression, and two views rotated in depth
by 15 and 30 degrees respectively.
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Technical Aspects
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The CPU time needed for the recognition
of one face against a gallery of 111
models is approximately 10--15 minutes
on a Sun SPARCstation 10-512 with a 50
MHz processor.
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Results-Office Items
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Comparison of Two Galleries
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More Results
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More Results-cnt’l
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Recognition Results Against
Galleries
Recognition results against a gallery of 20, 50, and 111 neutral frontal views
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Conclusion
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Close to natural model: a small number
of examples is needed for face
recognition
Gabor Wavelets representation are robust
to moderate lighting changes, shifts and
deformations
Elastic Graph Matching in Dynamic Link
Architecture is robust in face recognition
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Conclusion
1. Having only several images per person in
gallery does not provide sufficient
information to handle 3D rotation
2. Rectangle grid v.s. Feature points
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References
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M. Lades, J.C. Vorbruggen, J. Buhmann, J.
Lange, C. von der Malsburg, R.P. Wurtz, W.
Konen. Distortion Invariant Object Recognition
in the Dynamik Link Architecture. IEEE
Transactions on Computers 1992, 42(3):300311.
Laurenz Wiskott, Jean-Marc Fellous, Norbert
Krüger, et al. Face Recognition by Elastic Bunch
Graph Matching, Proc. 7th Intern. Conf. on
Computer Analysis of Images and Patterns,
CAIP'97, Kiel
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