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