This document summarizes a technique for recognizing facial emotions using statistical shape and texture analysis. It involves labeling landmark features on faces, modeling shape variations using PCA, fitting an active shape model to align faces, and generating mean models for the seven universal emotions of joy, sadness, surprise, fear, anger, disgust, and neutral. The methodology is tested on 70 training images from the Extended Cohn-Kanade dataset to recognize emotions based on facial features.

1. A Statistical Shape and Texture-
based Technique for
Recognizing Facial Emotions
Anukriti Dureha
7CSE2
A2305210153

2. Project Objective
To be able to recognize one of the seven universal emotions from a facial image
based on different facial features.
Neutral
Joy
Sadness
Surprise
Fear
Anger
Disgust
Seven Universal
Expressions

3. Project Methodology

4. Labeling
Landmark
Features
Eye
Brows
Nose
LipsChin
Eyes
No. of training images 70
Total no. of landmark
points
72
Face profile Frontal Faces
Datasets used Extended Cohn- Kanade
Example of a
hand–labeled
image

5. Shape Modeling

6. Shape Alignment
A self-Written
Module

7. Principle Component Analysis
A self-Written
Module

8. Model Fitting-ASM Search
• Initialize shape parameters to zero.
• Generate the model point positions.
• Find pose parameters using
procrustes analysis
• Projection of target image points
into model frame
• Update shape parameters.
• Iterate until convergence.

9. Results-Alignment of Shapes
Original Set of
Images
Aligned
Set of
Images

10. Results-Mean Models
Joy
Angry
Fear
Surprise Sad
Neutral

11. THANK YOU!