Zelun Luo and Anarghya Mitra created a robust face identification system under professors Jia-Bin Huang and Narendra Ahuja at the University of Illinois. The system uses an integral image and cascade architecture with Haar-like features to identify faces. It can identify multiple faces in an image and faces not in its original database by using a learning algorithm. The integral image allows features to be computed rapidly in constant time, while the cascade structure rejects most non-face sub-windows early.

1. Zelun Luo, Anarghya Mitra
Mentor: Jia-Bin Huang. Advisor: Narendra Ahuja.
Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
Make a robust system capable of identifying multiple faces with a
learning algorithm for identifying faces not in its database.
Integral Image
Cascade Architecture
Haar-like Features
• Very few faces in an image
• Most sub-windows rejected
early since they are not
faces
• Each successive classifier
is trained only on those
selected samples which
pass through the
preceding classifiers.
s(1) = A
s(2) = A + B
s(3) = A + C
s(4) = A + B + C + D
The sum within D can be
computed as:
s(4)+s(1)-s(2)-s(3).
(x, y)
A B
C D
1 2
3 4
The value of the integral image
at point (x, y) is the sum of all
the pixels above and to the left
of x, y, inclusive:
where s(x, y) is the integral
image and i(x, y) is the original
image.
• The area around the
eyes is lighter than
the eyes itself – i.e.
the nose is brighter
than the eyes on a
normalized graph.
• The area on top and
below the eyes is
lighter than the
eyes.
Our face can be used to control
computers. One example is a
game we can play with our
nose acting as the mouse.
Main ideas -
• Very few faces in an image => cascade structure of weak
classifiers
• Evaluate haar-like features in O(1) time using integral
image
• Select discriminative features with Adaboost
[Viola & Jones, 2004] Viola, P. & Jones, M. J. (2004). Robust real-time face
detection. International journal of computer vision, 57(2), 137–154.
[Yang, 2002] Yang, M.-H. (2002). Kernel eigenfaces vs. kernel fisherfaces:
Face recognition using kernel methods. In Proceedings of the Fifth IEEE
International Conference on Automatic Face and Gesture Recognition
Original images
• Eigenfaces encode the variation
in the training set.
• Each image can be represented
using a linear combination of
eigenfaces.
Can be used in identity
verification along with
fingerprint and iris recognition
systems.
Reconstruction
using eigenfaces