FACIAL EXPRESSION RECOGNITION IN PERCEPTUAL COLOR

SPACE

OBJECTIVE:

The main objectives of this project is an findout the human Emotion From

Human Grayscale Image (B/W Image Or color Image) using an FER(Facial

Expression Reorganization) Method’s Using an Data mining Technique.

PROBLEM DIFINITION:

The main problem found in our existing system as, classification systems

designed to output one emotion label per input utterance may perform poorly if the

expressions cannot be well captured by a single emotional label and Multiple

Algorithm Need for Finding the Human-emotion.

ABSTRACT:

In human-human communication the face conveys a lot of information.

People are identied by their face and it also has a strong eject on rest impressions.

We can recognize gender, estimate age, or deduce some cultural characteristics.

Analyzing faces in human-computer communication is also becoming increasingly

important. Ancient face representation is a key to any further analysis.

From face detection, through face and facial feature tracking, to face

classification problems (face recognition, gender, age, race, facial expression

detection), there have been various face representations used, all of them having

their advantages in their specific domain In this paper we present a novel face

representation for determining the color of various facial features, like skin, hair

and eyes. In order to cope with the numerous complicating external factors like

varying lighting conditions and camera settings, the full color range of the

segmented face image will be reduced to color categories based on human

cognition principles Such a representation of colors in face-images makes it easier

to extract the color of a given The effectiveness of color information on FER using

low-resolution and facial expression images with illumination variations is

assessed for performance evaluation.

Experimental results demonstrate that color information has significant

potential to improve emotion recognition performance due to the complementary

characteristics of image textures. Furthermore, the perceptual color spaces CIELab

and CIELuv) are better overall for FER than other color spaces, by providing

moreefficient and robust performance for FER using facial images with

illumination variation.

EXISTING SYSTEM:

Identify unique feature from the face image, extract and compare. The

purpose of the project is to compare the face image of a person with the existing

face images that are already stored in the database.

DISADVANTAGES:

Classification systems designed to output one emotion label per input

utterance may perform poorly if the expressions cannot be well captured by

a single emotional label.

Multiple Algorithm Need For Finding the Human-emotion.

PROPOSED SYSTEM:

This paper introduces a novel tensor perceptual color framework (TPCF) for

FER based on information contained in color facial images, and investigates

performance in perceptualcolor space under slight variations in illumination.The

imagebased FER systems consist of several components.

Face Detection and Normalization

Feature Extraction

Feature Selection

Classification

ADVANTAGES:

Easily to findout the human Facial Expression

There is no need of an Any Clustring Technique for finding the human

Expression on the human image.

ALGORITHM USED:

1. RGB / Skin Tone Detection

2. Feature Selection

3. Feature Extraction

ARCHITECTURE DIAGRAM:

SYSTEM REQUIREMENTS:

Hardware Requirements:

• System : Pentium IV 2.4 GHz.

• Hard Disk : 40 GB.

• Monitor : 15 VGA Colour.

• Mouse : Logitech.

• Ram : 512 Mb.

Software Requirements:

• Operating system : Windows XP.

• Framework : Visual Studio 4.0.

• Coding Language : ASP.Net with C#.

• Data Base : SQL Server 2005.

APPLICATIONS:

1. Digital Cam

2. Deaf & Dumb Schools