A NEURAL ADABOOST BASED FACIAL

EXPRESSION RECOGNITION SYSTEM

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

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

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

SOME PREVIOUS WORKYear Feature

ReductionFeature

ExtractionClassification 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.

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

HOW PROPOSED TECHNIQUE WORKS

Face detection and image down-sampling

Gabor feature extraction

Feature selection

Multilayer feed forward neural network(MFNN)

FACE DETECTION AND DOWN-SAMPLING

Face Detection component was implemented by Viola Jones.

Image is rescaled to 20 * 20px by Bessel Down Sampling.

GABOR FEATURE EXTRACTION

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

MULTILAYER FEED-FORWARD NEURAL NETWORK (MFFNN) CLASSIFIER

TRAINING ALGORITHM

Process of Training Involves Weight Initilization Calculation of Activatin Function Weight Adjustment Weight Adaption Testing for Convergence of N/W

TRAINING ALGORITHM Training Algorithm Modeled as:

Activation Funciton of Hidden Units:

Activation Function of Output Units:

Network Error Function

HOW TO MINIMIZE THE ERROR

To minimize the error, each weight in the network need to be computed.

Previous Weight Changes:

WEIGHT UPDATTION

RESULT ON JAFFEE

RESULT ON YALE

GRAPHICAL REPRESENTATION (JAFEE)

GRAPHICAL REPRESENTATION (YALE)

COMPARATIVE RESULTS(JAFEE)

COMPARATIVE RESULTS(YALE)

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

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.