Publication Date

Spring 2016

Document Type

Project Summary

Degree Name

Master of Science

Department

Computer Science

First Advisor

(Clare) Xueqing Tang, Ph.D.

Second Advisor

Soon-Ok Park, Ph.D.

Third Advisor

Do Young Park, Ph.D.

Abstract

A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Face recognition devices are ideal for such systems, since they have recently become faster, cheaper. When combined with voice-recognition, they are very robust against changes in the environment. Moreover, since humans primarily recognize each other by their faces and voices, they feel comfortable interacting with an environment that does the same.

Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and create a unique file called a "template." Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and pre-existing photos such as those in driver's license databases.

These systems depend on a recognition algorithm, such as the hidden Markov model. The first step for a facial recognition system is to recognize a human face and extract it for the rest of the scene. Next, the system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features.

In this project, we describe Locality Preserving Projection (LPP), a new algorithm for learning a locality preserving subspace. The complete derivation and theoretical justifications of LPP can be traced back to. LPP is a general method for manifold learning. It is obtained by finding the optimal linear approximations to the Eigen functions of the Laplace Beltrami operator on the manifold. These nodal points are then compared to the nodal points computed from a database of pictures in order to find a match. Obviously, such a system is limited based on the angle of the face captured and the lighting conditions present.

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