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1. A NEURAL ADABOOST BASED
FACIAL EXPRESSION
RECOGNITION SYSTEM

2. BIRD EYE VIEW ABOUT THIS PAPER
 Object Detection Framework(Viola-Jones Descriptor)
 Down Sampled by Bessel Transform
 Gabor Featrue Extraction Technique employed
 Select numerous features using AdaBoost hypothesis
 Neural Network Backpropogation algorithm use for
classification.
 Tested on JAFEE and Yale Facial Expression
Database
 Avergae Recogniton Rate is 96.83% and 92.2%.
 Execution time for 100 Х 100 pixel is 14.5ms

3. THE PAPER
 Title: A neural AdaBoost based facial expresson
recognition system
 By: E.Owusu, Y. Zhan, Qi Rong Mao
 From: Jiangsu University, China
 Year: 2014
 Citations: 12
 Published: Expert System With Applications
 Reference: http://www.sciencedirect.com/science/article/pii/S0957417413009615

4. INTRODUCTION
 Facial expression involves application of AI.
 It is related to Patten recognition and computer vision
 Facial expression are seven prototypical ones, namely
 Anger, fear, surprise, sad, disgust, happy, neutral
 This technology is applied in various fields like robotics,
mobile applications, digital signs e.g.
 AIBO Robot Biologically inspired robots
 Some robots can display happiness feeling when detect face.
 Databses: JAFFEE and Yale

5. SOME PREVIOUS WORK
Year Feature
Reduction
Feature
Extraction
Classification Performance
2001 PCA FFNN 84.5%
2012 PCA GFE NN 60-70%
2012 AMI FFNN 93.8%
2007 Sobel Filter Elmon N/W 84.7%
2009 PCA GFE NN 93.4%
 In Most of the Studies:
 Expression Classifier: Neural Network
 Extracted Features: Gabor Filter
 Feature Reduced: PCA
 Displeasing is that the result is not encouragable.

6. PROPOSED TECHNIQUE
 Data reduced by Bessel Transformation.
 Extraction of the face by Gabor Methods
 Feature Reduced by AdaBoost Feature Reduction Technique
 Facial Expression Recognition using Bessel down
Sampling
 Classifier is Multi layer feed forward neural network
using backpropogation

7. HOW PROPOSED TECHNIQUE WORKS
 Face detection and image down-sampling
 Gabor feature extraction
 Feature selection
 Multilayer feed forward neural network(MFNN)

8. FACE DETECTION AND DOWN-SAMPLING
 Face Detection component was implemented by
Viola Jones.
 Image is rescaled to 20 * 20px by Bessel Down
Sampling.

9. GABOR FEATURE EXTRACTION

10. FEATURE SELECTION
 Selection Algorithm
 Initialize Sample Distribution
 For the iteration t = 1, 2,..., T, where T is the final iteration
 Normalize the Weight
 Train a weak Clasifier
 Select the hypothesis
 Compute the weight
 Update the weight distribution
 Final Selection feature Hypothesis

11. MULTILAYER FEED-FORWARD NEURAL
NETWORK (MFFNN) CLASSIFIER

12. TRAINING ALGORITHM
 Process of Training Involves
 Weight Initilization
 Calculation of Activatin Function
 Weight Adjustment
 Weight Adaption
 Testing for Convergence of N/W

13. TRAINING ALGORITHM
 Training Algorithm Modeled as:
 Activation Funciton of Hidden Units:
 Activation Function of Output Units:
 Network Error Function

14. HOW TO MINIMIZE THE ERROR
 To minimize the error, each weight in the network
need to be computed.
 Previous Weight Changes:

15. WEIGHT UPDATTION

16. RESULT ON JAFFEE

17. RESULT ON YALE

18. GRAPHICAL REPRESENTATION (JAFEE)

19. GRAPHICAL REPRESENTATION (YALE)

20. COMPARATIVE RESULTS(JAFEE)

21. COMPARATIVE RESULTS(YALE)

22. CONCLUSION
 This study employs advance techniques
 Improve recognition rate and execution time
 Study Involves
 Face Detection: Viola Jones Descriptor
 Down Sampled: Bessel Transform
 Extracted Feature: AdaBoost Algorithm
 Select Feature: Gabour Wavelets
 Selected Feature fed into MFFNN Classifier
 Network trained by sample database JAFEE and
Yale

23. EXECUTION TIME AND RECOGNITION RATE OF
PROPOSED METHOD
 Previous Performance
 The execution time for a pixel of size 100 x 100 is 14.5
ms; the average recognition rate in JAFFE database is
96.83% and that in Yale is 92.22%.
 Proposed Method
 Study shows that
 Automatic expression recognitions are very accurate in
surprise, disgusts and happy about 100%.
 Mild expressions like sad, fear and neutral have lower
accuracies.