1. 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

2. 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.

3. 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

4. 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