The document outlines a system for emotion detection from frontal facial images aimed at enhancing human-computer interaction, identifying emotions such as neutral, happy, and sad. It discusses various aspects including the selected color models (HSV and YCbCr) for feature extraction, existing approaches, and the chosen methods (Gabor wavelet transform and artificial neural networks) for implementation. Future work includes completing the system and analyzing its performance.

1. Emotion Detection
from Frontal Facial
Image Using
Feature Extraction
Sakib Hussain
ID-09101004

2. OUTLINE
• Problem Description
• Applications of the System
• Workflow
• Selecting Color Model
• Existing approaches and their performance
• My Approach
• Future Plan

3. Problem Description
• A system to enhance HUMAN-COMPUTER
interaction.
• Emotion Detections (Neutral, Happy, Sad,
Angry, Surprised) from an given image.
• Detect Face from the image.
• Extract Feature from the detected face.
• Comparing Different existing Methods to
determine a good method to work with.
• Implement the System.

4. Application of the System

5. Total Workflow

6. Color Model
• RGB
• HSV(hue, saturation, and value)
• Binary
• YCbCr
• CIE XYZ
• CMYK
HSV and YCbCr color models are used for Maximum Accuracy of the
Face and feature Extraction. I have Used HSV to detect Face from
Image

7. Previous Works
There are many ways or approaches to Extract Features from a Face image.
• Probabilistic Approach
• Deterministic Approach
Algorithms or Methods that are Used :
• PCA
• LDA
• Adaptively Weighted sub-Pattern PCA
• Neural Network
• Hidden Markov Model
• Gavor Wavelet Transform

8. Previous Works

9. My selected Approach
• Gavor Wavelet Transform
for Feature Extraction.
• Artificial Neural Network For
training and Obtaining Result from
Features that are Extracted.

10. Future Work
• Implementation of Extraction of features
from cropped image.
• Complete the proposed System.
• Analyze Performance of the proposed
system.

11. THANK YOU
Any Questions?