This document presents a method for facial expression recognition based on image features. It discusses existing works that use techniques like PCA and Gabor wavelets for feature extraction and Euclidean distance for classification. The proposed method uses Gaussian filtering, radial symmetry transform, and edge projection for feature extraction, and calculates a feature vector based on geometric facial parameters to classify expressions using Euclidean distance. It aims to recognize six basic expressions accurately from the JAFFE database and could be developed for real-time video recognition in the future.

1. FACIAL EXPRESSION RECOGNITION
BASED ON
IMAGE FEATURE
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
ANWESHA PAUL
ID: 110206
TASNIM TARANNUM
ID: 110216
PRESENTED BY:

2. LOGO2
Overview
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Facial Expression &
Facial Expression Recognition
 Related Works
 Problems Of Existing System
 Motivation
 Proposed Method
 Conclusion
 Reference

3. LOGO3
Facial Expression
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Powerful, natural and immediate means for human to
communicate their emotions.
 Vital part of communication.
 Widely recognized in social interaction.

4. LOGO4
Facial Expression
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Neutral Happy Surprise Sad Disgust Angry Fear
Fig 1: Basic Facial Expression.

5. LOGO5
Expression Recognition
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Locating faces in the scene.
 Extracting facial features.
 Analyzing the motion of facial feature.

6. LOGO6
Expression Recognition
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Face
Acquisition
Facial Data
Extraction &
Representation
Facial
Expression
Recognition
Face
Detection
Head
Pose
Estimation
Feature-
based
Appearance
-based
Frame-
based
Sequence
-based
Fig 2: Basic Structure of Facial Expression Recognition.

7. LOGO7
Related Work
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Reference
Image
Acquisition
Feature
Extraction
Classification
Recognition
Performance
Neeta Sarode
et al.
[1]
Gray scale
image to
recognize four
expressions
2D appearance-
based local
approach
Euclidean
distance
Accuracy rate
81%
Rupinder
Saini et al.
[2]
PCA, Gabor
wavelet, PCA
with SVD
Euclidean
distance, PCA
[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer Science and
Engineering, Vol. 02, No. 05, 2010.
[2] Rupinder Saini, Narinder Rana, “Facial Expression Recognition Techniques, Database & Classifiers”, International Journal of
Advances in Computer Science and Communication Engineering (IJACSCE), Vol. 2, Issue 2, June 2014.

8. LOGO8
Related Work contd..
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Reference
Image
Acquisition
Feature
Extraction
Classification
Recognition
Performance
Jeemoni
Kalita et al.
[3]
60 samples with
various
expression of
RGB color
image
Manually
extracted and
Eigenvector
based distributed
feature
Euclidean
distance
Recognition
rate 95% &
process time
0.0295 sec
Ajit P.Gosavi
et al.
[4]
Real database
image to
recognize five
basic emotions
PCA (Principal
Component
analysis) with
SVD (Singular
Value
Decomposition)
Euclidean
distance
Avg. accuracy
89.70% & avg.
recognition rate
65.42%
[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean
Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 4, No. 2, 2013.
[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value
Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue 6,
November 2013.

9. LOGO9
Related Work contd..
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Reference
Image
Acquisition
Feature
Extraction
Classification
Recognition
Performance
Akshat
Garget et al.
[7]
Gray scale
image
PCA (Principal
Component
analysis)
Euclidean
distance & PCA
Accuracy rate
89.0%
Mahesh
Kumbhar et
al.
[8]
JAFFE [6]
database image
PCA(Principal
Component
analysis), Gabor
wavelet
Euclidean
distance
Recognition
rate 60% to
70%
[
7] Akshat Garg, Vishakha Choudhary, “Facial Expression Recognition Using Principal Component Analysis”, International Journal
of Scientific Research Engineering &Technology (IJSRET), Vol. 1 Issue4, July 2012.
[8] Mahesh Kumbhar, Ashish Jadhav, Manasi Patil, “Facial Expression Recognition Based on Image Feature”, International
Journal of Computer and Communication Engineering, Vol. 1, No. 2, July 2012.
[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http:// www.kasrl.org/jaffe.html

10. LOGO10
Problems Of Existing System
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Don’t contain enough feature points.[1]
 PCA-based face recognition systems are hard to scale
up.[4]
 Color image burdensome.[4]
[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on Computer
Science and Engineering, Vol. 02, No. 05, 2010.
[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with Singular Value
Decomposition”, International Journal of Advance Research in Computer Science and Management Studies Vol. 1, Issue
6, November 2013.

11. LOGO11
Problems Of Existing System
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Cropping manually is time killing.[3]
 Inabilities (different angles and different reasons).
[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed Features
and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal of Advanced
Computer Science and Applications, Vol. 4, No. 2, 2013.

12. LOGO12
Motivation
COMPUTER SCIENCE & ENGINEERING
 Recognize facial expression as like a human.
 Recognize six basic expressions.
 Increase the accuracy rate.

13. LOGO13
Proposed Method
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Block diagram of proposed system:
Image
Acquisition
Feature
Extraction
Classifier
Happy
Sad
Surprise
Angry
Fear
Disgust
Fig 3: Block diagram.

14. LOGO14
Proposed Method
contd….
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Image Acquisition:
Convert the color image into gray scale image.
Fig 4: RGB- color image converted into gray scale image.

15. LOGO15
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Feature Extraction:
• Gaussian filter.
• Radial Symmetry Transform ∗.
Fig 5: application of Gaussian filter.

16. LOGO16
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Feature Extraction:
• Edge projection ∗.
• Segmentation using Laplacian of Gaussian
operator at zero threshold.
.

17. LOGO17
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Classifier:
• Euclidean distance based on geometrical
relationship.
• The feature vector V.
𝑉 = 𝑉𝑑0 𝑉𝑑1 𝑉𝑑2 𝑉𝑤 𝑉ℎ 𝑉𝑢𝑙 𝑉𝑙𝑙

18. LOGO18
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Classifier:
Here,
Vd0 = distance of eyebrow,
Vd1 = distance between right eyebrow and nose tip,
Vd2 = distance between left eyebrow and nose,
Vw = mouth width,
Vh = mouth height,
Vul = upper lip curvature,
Vll = lower lip curvature.
Fig 6: Geometrical parameters of the face, forming the feature vector.

19. LOGO19
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Decision Making Techniques:
• Feature vector calculation.
• Observe each component of feature vector.
• Comparison between testing image and neutral
image.

20. LOGO20
Proposed Method
contd…
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Result Analysis:
Comparison between the testing image with it’s
corresponding images from training database [6].
[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http://
www.kasrl.org/jaffe.html

21. LOGO21
Conclusion
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
 Recognize six basic facial expressions.
 Future work: Will develop the same in real time
videos.

22. LOGO22
Reference
COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
[1] Neeta Sarode, Prof. Shalini Bhatia, “Facial Expression Recognition”, (IJCSE) International Journal on
Computer Science and Engineering, Vol. 02, No. 05, 2010.
[2] Rupinder Saini, Narinder Rana, “Facial Expression Recognition Techniques, Database & Classifiers”,
International Journal of Advances in Computer Science and Communication Engineering (IJACSCE),
Vol. 2, Issue 2, June 2014.
[3] Jeemoni Kalita, Karen Das, “Recognition of Facial Expression Using Eigenvector Based Distributed
Features and Euclidean Distance Based Decision Making Technique”, (IJACSA) International Journal
of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013.
[4] Ajit P.Gosavi and S.R. Khot, “Facial Expression Recognition uses Principal Component Analysis with
Singular Value Decomposition”, International Journal of Advance Research in Computer Science and
Management Studies Vol. 1, Issue 6, November 2013.
[6] JAFFE (Japanese Female Facial Expression) Face Database. Available: [Online] http://
www.kasrl.org/jaffe.html
[7] Akshat Garg, Vishakha Choudhary, “Facial Expression Recognition Using Principal Component
Analysis”, International Journal of Scientific Research Engineering &Technology (IJSRET), Vol. 1
Issue4, July 2012.
[8] Mahesh Kumbhar, Ashish Jadhav, Manasi Patil, “Facial Expression Recognition Based on Image
Feature”, International Journal of Computer and Communication Engineering, Vol. 1, No. 2, July
2012.

23. COMPUTER SCIENCE & ENGINEERING
KHULNA UNIVERSITY
Thank you.