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) “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”.

Facial Scans 3 processes of facial scan: 1. feature extraction 2. search key creation 3. matching

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

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) (black) (white) (Bebis, 2001) feature vector = {n(x = 0)/total, n(x=1)/total, …, n(x=255)/total}

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)

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)

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

Hashing Grid File: - Divide the space into grids arbitrary - Each grid becomes a key of searching A grid represents a group of similar images Image data

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

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

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

Reference Systems Biophysics, the Institut für Neuroinformatik (INI), bochum.de/ini/top.html Wired News, Lycos Inc., Dr. George Bebis, Associate Professor, Computer Science of University of Nevada, Reno Dr. Carl Looney, Professor, Computer Science of University of Nevada, Reno