Human Face Reconstruction and Recongnition
A Linear Algebra Approach
Semester Project by Kevin Ng and Suresh Subbiah
Large-Scale Data Mining
In this project we have analyzed various schemes for automatic human face
reconstruction and recognition.
Both these problems have been well studied in literature but recently
reserch on these topics have taken a
new path. Following the 1990 paper by Sirovich and Kirby, linear algebraic
techniques have been used to
reduce the complexity of the problem. In this report, following Professor
Dhillon's suggestion, we have
introduced two computationally cheap methods to tackle the problems
of face reconstruction and recognition.
We call them Meanfaces and Svdfaces. We also compare the results obtained
by these methods with
results obtained by applying Eigenfaces and Fisherfaces methods to
the same dataset. Eigenfaces and
Fisherfaces are classical methods in this field now. References to
these approaches may be found
in the class reading list.
: Face Recognition, February 5th, 2000
Report : Human face Reconstruction using Eigenfaces, March
Final Report : Human face Reconstruction and Recognition,
May 8th, 2000
Software and Face databases
During the course of this project we have written software which can be
on MATLAB. There are three
major code segments. The first two are GUI based and allow the user
to see the faces being analyzed.
The last code segment is suitable for collecting data over large data
sets, by considering diffrent
partitions of the dataset into training and test faces. Each code segment
comes with a README file.
Meanfaces, SVDfaces GUI
We also provide links to the two face databases we used for the project
database (143 persons, 1 image each, 128 * 128 pixels, bmp format)
database (15 persons, 11 images each, 121 * 160 pixels, bmp format)