Image Databases for Face Recognition System

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Image Databases for Face Recognition System

• Yumiko Shironouchi

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Super Bowl XXXV 2000 Season

• Baltimore 34 NY Giants 7 (Jan. 28th, 2001)
• Attendance 71,921

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Call It Super Bowl Face Scan I (Wired News, 2001)

• When tens of thousands of football fans packed
  into a Florida Stadium for Super Bowl XXXV, they
  werent merely watching the game They were also
  being watched.
• Face-Recognition software surreptitiously scanned
  everyone passing through turnstiles and flashed
  probably matches with the mugs of known criminals
  on the screens of a police control room.

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Facial Scans

• 3 processes of facial scan
• feature extraction
• search key creation
• matching

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Feature Vector

• Three Main features of an image
• Color histogram
• Texture
• Shape of object
• It depends on applications which feature is
  extracted and converted into vector notations.
• Images that have similar feature vectors they
  are similar images

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Color Histogram

• Vertical values represents the number of pixels
  that have the corresponding pixel value.
• of pixel (value x)
• total of pixels
• one factor of feature vector (pixel value x)

0
255 (black)
(white)


(Bebis, 2001)
feature vector n(x 0)/total, n(x1)/total,
, n(x255)/total
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Graph (shape of face)

• Wavelet Transform
• divide an image into high-frequency ingredient
  and low-frequency ingredient
• extract of edges of object (face) analyzing
  low-frequency ingredient

upper original image lower
edge image (Looney, 2002)

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Graph (cont.)

• Pick up the feature points (eyes, nose and mouth)
  from the edge image to make a graph
• Convert into a vector distance (or ratio to a
  unit distance ) to neighbor nodes and the angles
  between each edge

(Systems Biophysics, 2001)
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For the efficient searching

• Grouping images is necessary for faster search
• Two access ways
• - hashing (Grid Files)
• - indexing (R-Tree)

feature vector of an image
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Hashing

• Grid File
• Divide the space into grids arbitrary
• Each grid becomes a key of searching

Image data
A grid represents a group of similar images
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Indexing

• R-tree
• Grouping k (some positive integer) nearest images
  from a point (nearest k points search)

Above graph is shown in 2-dimensional, but
actually it is in multi-dimensional
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• representative vector
• the center of feature vectors of images in the
  group
• Groups of images are sorted and searched using
  the representative vectors.

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Image Data Flow
Store or search
Grey arrow flow of the creation of image
database White arrow flow of the search of
similar images
Database
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Reference

• Systems Biophysics, the Institut für
  Neuroinformatik (INI), 2002
• http//www.neuroinformatik.ruhr-uni-bochum.de/ini
  /top.html
• Wired News, Lycos Inc., 2002
• http//www.wired.com/
• Dr. George Bebis, Associate Professor, Computer
  Science of University of Nevada, Reno
• Dr. Carl Looney, Professor, Computer Science of
  University of Nevada, Reno