Distinguish Genuine and Posed Emotions using Computer Vision Deep Learning Approach

J I A L I N YA N G

U 5 8 9 4 1 0 0

C O M P 4 5 6 0

S U P E R V I S E D BY

D R . TO M G E D E O N ;

D R . Z A K I R H O S S A I N ;

M R . M O S H I U R FA R A Z I

Outliine

§ Introduction§ Method§ Conclusion§ Reference

Introduction

Introduction – Our Goal

I can tell ya, This guy is fake his smiling.

Be careful of him!

Some existing methods and theiraccuracy

Our Method

3-Steps:

1. Generating 3D mesh from2D input image

2. Render the mesh and useemotion detection to decidethe potential of the emotion

3.Evaluate the accuracy of ourmethod.

First Step:

2D Image to 3D Mesh

68 Dots captured

More dots will capture more details but increase the running speed.

Second Step:

Render 3D mesh into 2D Image

Using Blender currently

Using Deep Learning network to distinguish the emotion possibilities.

30% Smile10% Angry5% Sad

Neural Network withpretrained model

Third Step:

Evaluation of our method

See its accuracy compared to other models

Conclusion

§ New distinguished method – Introduce the use of 3D mesh generating and rendering before applying the Neural network

§ The accuracy result relies on the accuracy of the 3D mesh generator

§ Expecting a higher accuracy compared to other deep learning methods

ReferenceKim, Y., & Huynh, X. (2017). Discrimination Between Genuine Versus Fake Emotion Using Long-Short Term Memory with Parametric Bias and Facial Landmarks. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 3065-3072.

Jianzhu.Guo, 3DFFA, (2019), GitHub repository, https://github.com/cleardusk/3DDFA