1. Object Recognition in the Dynamic Link Architecture
Yang Ran
CMPS 828J

2. Outline Background and Introduction System Overview
General algorithm in details
Implementations of the algorithm
Experiment results
Further readings and conclusion
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3. Background
Problem: To recognize human faces from single images our of a large gallery.
Challenges: Distortions in terms of position, size , expression, and pose
Existed methods:
Appearance Based v.s. Shape based
2D vs. 3D
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4. Background: Notations
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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5. System Overview
Faces are represented as rectangular graphs by layers of neurons
Each neuron represents a node and has a jet attached
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6. 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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7. Key Factors
Basic representation is the labeled graph formed by edges and vertices bundled in jets
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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8. Preprocessing by Gabor Wavelets
Gabor Wavelets are biological motivated convolution kernels in the shape of plane waves restricted by Gaussian envelope function
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9. More for Gabor Why use it?
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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10. Jets Generation
The set of convolution coefficients for kernels and frequencies at one image pixel is called a jet
Describes a small patch of gray values around a given pixel
Sample W at five logarithmically spaced f levels and eight directions by u, v
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11. Jets Generation-cnt’l
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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12. Edge Labels
Derived from neuron version, edges encodes neighborhood relationships
Presents the topology of the vertices
Define
Quadratic comparison function
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13. Example
Graph representation of a face
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14. 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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15. 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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16. Experiments
Data Base
Technical Aspects
Results
Conclusions
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17. 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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18. Technical Aspects
The CPU time needed for the recognition of one face against a gallery of 111 models is approximately minutes on a Sun SPARCstation with a 50 MHz processor.
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19. Results-Office Items
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20. Comparison of Two Galleries
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21. More Results
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22. More Results-cnt’l
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23. Recognition Results Against Galleries
Recognition results against a gallery of 20, 50, and 111 neutral frontal views
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24. Conclusion
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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25. Conclusion
Having only several images per person in gallery does not provide sufficient information to handle 3D rotation
Rectangle grid v.s. Feature points
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26. References
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):
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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