Binning and Indexing Biometric Records

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Binning and Indexing Biometric Records

• Sharat S. Chikkerur
• CUBS, University at Buffalo
• ssc5_at_eng.buffalo.edu

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Problem Description

• Biometrics are being deployed for immigration and
  national ID applications
• US-VISIT program
• Voter ID and national ID programs3
• Potential size that can run into millions
• Largest study by NIST considers only 620,000
  records4
• Apart from accuracy speed and efficiency also
  become important at this scale
• Only biometric identification (1N matching) can
  prevent duplicate enrollments

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Problem Description (cont.)

• In biometric templates, there is no natural
  order by which one can sort the biometric
  records
• Biometric Templates are inherently higher
  dimensional
• Semantic features are not stored in the template

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Identification Problem

• Let FAR and FRR be the false acceptance rate and
  false reject rate for 11 matching
• For a 1N matching,

• The total number of false accepts is given by

• Even if FAR 0.0001, False accepts 1 in 10
  for N100000(lower bound)
• No single biometric is capable of meeting this
  security requirement individually

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Uses of Indexing and Binning

• Ways to reduce identification errors
• Reduce N
• Reduce FAR (Limited by technology)
• We can reduce N by pruning the records
• Let PSYS Penetration rate

• For a 1N matching,

• The total number of false accepts is given by

• State of the art fingerprint systems has PSYS0.5
  6

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Indexing and Binning(cont.)

• Will allow us to screen immigrants at airports
  against a watch list
• Will make biometric systems more user-friendly by
  eliminating the need to remember PINs and Ids
• Will improve accuracy (FARN) and performance

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Binning Biometric Data

• Vector Quantization Approach

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Vector Quantization

• What is Vector Quantization
• A data clustering method
• Implementations
• K-means clustering
• LVQ- Learning vector quantizer
• Applications of Vector Quantization
• Image Compression
• Speaker Identification
• Voice Compression(vocoding)

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Vector Quantization(cont.)

• In general a biometric template may be
  represented as a vector
• The objective is to classify the vectors into N
  distinct classes(code book vectors)
• The code book vectors divide the feature space
  into N distinct Voronoi regions
• Properties of the regions

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Vector Quantization-Voronoi Regions
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Hand Geometry- Template Model
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Experimental Evaluation

• 25x10 hand geometry features used
• Each print represented by a 21D vector
• Data divided equally among training and testing
• Data is normalized using

• VQ is implemented using k-means clustering
• The codebook vectors are used on the test set

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Normalization

• Observations
• Data normalization leads to spreading of data
• Without norm., clusters converge to a single
  center
• Equivalent to measuring Mahalanobis distance5
• Difference instances of the same had
  misclassified

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Preliminary Results
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Indexing Biometric Data

• Spatial Access Methods Approach

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Introduction to Spatial databases

• Relational databases organize and store scalar
  data
• Has planar organization
• Contains scalar data (excluding LOBs, binary)
• Data can be ordered linearly
• Structured Query Language used to retrieve
  records
• Spatial databases
• Contain multi-dimensional or vectorial data
• Relative positions may be explicit or inferred
• Linear proximity does not imply spatial proximity
• Multi dimensional data is used in computer
  vision, medical imaging, and BIOMETRICS
• Original Applications
• Point sets
• CAD
• VLSI drawings
• Cartography, astronomy

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Spatial databases (cont.)

• Difference from pattern classification QUERIES
• Spatial searches
• Neighborhood searches
• PAM/SAM
• Point Access Methods
• Used on point databases
• Points may be multi-dimensional (Vectors)
• Points have spatial extents, intersection
  undefined
• Each point is specified uniquely by its d
  co-ordinates
• Spatial Access Methods
• Used on lines, polygons, solids
• Have spatial extent, intersection of objects well
  defined
• A point may be occupied by more than one object

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Problems with vectorial/spatial data

• No standard algebra defined on spatial data
• Union, intersection, union not defined exactly
• Data operations highly application specific
• Operators are not closed
• Queries
• Need support for spatial queries point and
  region queries
• No standard spatial query language
• No natural ordering
• Ordering that preserves spatial proximity does
  not exist
• No mapping between multi-dimensional space to 1D
  such that two points that are close together in
  higher dimensional space are also closed
  linearly1
• Is it possible to do this via PCA/KLT?
• Cannot extend single key structures like B-Tree

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Requirements of a spatial database

• Dynamic updates
• The structure should be consistent as data is
  inserted and deleted
• Changes should be tracked
• Independence of input data and insertion sequence
• Should handle skewed data
• Structure should be independent of insertion
  sequence(Compare tree)
• Scalable
• Efficiency
• Time Efficiency
• Efficient design will approach the performance of
  B-Trees
• Space Efficiency
• Indexing overhead should be small

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Types of structures

• K-d Trees
• Binary tree in d-dimensional space
• d-1 hyperspaces separate the subspaces
• The directions alternate among the
  d-possibilities
• Insertion and search are straight forward
• Deletion is cumbersome
• Structure is sensitive to insertion order

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References

• Gaede and Gunther, Multidimensional Access
  Methods, ACM Computing Surveys, Vol.30, No.2,
  1998
• www.geocities.com/mohamedqasem/
  vectorquantization/vq.html
• Bolle et al. Guide to Biometrics, Springer
  Verlag, 2003
• NIST report to the United States Congress,
  Summary of NIST Standards for Biometric
  Accuracy, Tamper Resistance and
  Interoperability, http//www.itl.nist.gov/iad/894
  .03/NISTAPP_Nov02.pdf
• http//www.galactic.com/Algorithms/discrim_mahaldi
  st.htm
• Dr.Waymans report, NIST

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Thank You

• ssc5_at_cedar.buffalo.edu