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Image Databases for Face Recognition System

Image Databases for Face Recognition System. Yumiko Shironouchi. Super Bowl XXXV 2000 Season. Baltimore 34 – NY Giants 7 (Jan. 28th, 2001) Attendance: 71,921. Call It Super Bowl Face Scan I (Wired News, 2001).

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Image Databases for Face Recognition System

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  1. Image Databases for Face Recognition System Yumiko Shironouchi

  2. Super Bowl XXXV 2000 Season • Baltimore 34 – NY Giants 7 (Jan. 28th, 2001) • Attendance: 71,921

  3. 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 weren’t 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”.

  4. Facial Scans 3 processes of facial scan: • feature extraction • search key creation • matching

  5. 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

  6. 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(x=1)/total, …, n(x=255)/total}

  7. 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)

  8. 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)

  9. 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

  10. 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

  11. 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

  12. representative vector: the center of feature vectors of images in the group Groups of images are sorted and searched using the representative vectors.

  13. 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

  14. 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

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