Searching and Browsing Video
in Face Space
Lee Begeja
Zhu Liu
Video and Multimedia Technologies Research
Face Oriented Video Browsing
• Challenge - non linguistic browsing
• Browse a video using faces
• Anchorpersons in news broadcasts
• Main casts in movies
• Hosts and guests in talk shows
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Face Finding
• Face Detection
• Find a face
• Face Recognition
• Find a specific face
• Face Clustering
• Find a set of similar faces
Face Clustering
What’s in a Face ?
Feature extraction - 50 Features
•9 – 3 color moments in Luv space
• Moments – mean, variance, skew
• Luv – L -luminance; u,v–chrominance
•24 – Gabor textures – 3 scales x 4
directions, mean and std dev
•17 – Edge detection histogram in 16 bins
across the 2∏ polar coordinate space; with
one bin for non-edge pixels
Face Clustering
• Icon region alone: The
face dissimilarity is defined
as the icon region distance,
ID.
• Torso region alone: The face dissimilarity is defined as
the torso region distance, TD.
• Torso region and Icon region: The face dissimilarity is
defined as weighted summation of torso and icon region
distance, α∙TD + (1- α)∙ID, where α is the weighting
factor.
• Torso region and Face region: The face dissimilarity is
defined as the minimum of the torso region distance and
face distance based on eigenface projection,
min(TD, FD).
Video Browsing Interface
Page 7
Performance Metrics
•Average Cluster Purity (ACP) – perfect
ACP of 1.0 means each cluster only contains
faces from one person.
•Average Face (Class) Purity (AFP) –
perfect AFP of 1.0 would have all the faces of
one person appearing in one cluster.
•Analogous to precision(ACP) vs. recall(AFP)
Results
Face dissimilarity
Video 1
AFP
ACP
Video 2
AFP
ACP
Icon region alone
0.45
0.61
Torso region alone
0.47
0.82
0.57
0.92
Torso + Face regions
(α=0.5)
0.51
0.83
0.70
0.97
Torso + Face regions
(eigenface)
0.54
0.92
0.72
1
Future Work
•Working with Sumit Chopra to incorporate
dimensionality reduction (DrLIM)
• Face Search/Clustering across programs
•Discussions with Patrick Haffner on using
SVMs for Face Recognition
•Do specific face recognition (Obama, Leno)
•Search for multiple faces within a frame
• Improve Face Detection
•Include user generated video in our results
Additional Slides
Thatcher
Effect
Gabor textures
•3 scales x 4 directions
Directions
Scales
Eigenface
Eigenface approach is a PCA (Principal Component
Analysis) method, in which a small set of
characteristic pictures are used to describe the
variation between face images.
Recognition is performed by projecting a new image
onto the subspace spanned by the eigenfaces and
then classifying the face by comparing its position
in the face space with the positions of known
individuals.
Informally, eigenfaces are a set of "standardized
face ingredients", derived from analysis of many
pictures of faces. Any human face can be
considered to be a combination of these standard
faces. For example, a face might be composed of
the average face plus 20% from eigenface 1, 35%
from eigenface 2, and -12% from eigenface 3.
Eigenfaces