Introduction to Facial Micro Expressions Analysis

Introduction to Facial Micro Expressions Analysis Using Color and Depth Images (a Matlab Coding Approach)
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Updated 19 Jun 2023
-Publishing any sample of IKFDB is illegal.
-Samples inside the database could be used just in a scientific experiment and not for any other purposes.
If you used samples of this database in your experiment you have to cite it properly as bellow:
Mousavi, Seyed Muhammad Hossein, and S. Younes Mirinezhad. "Iranian Kinect face database (IKFDB): a color-depth based face database collected by Kinect v. 2 sensor." SN Applied Sciences 3.1 (2021): 1-17.
-In order to use the samples, it is required to send me a letter from your supervisor that it is going to be used just for your scientific experiment and responsibility of any other usage is with your supervisor.
My Email: mosavi.a.i.buali@gmail.com
-Important: publishing any sample from IKFDB into the web or any other publishing methods is illegal.
The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI.
Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field.
We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro expressions recognition, feature extraction and dimensionality reduction.
This book is product of several years of researches and experiments and reflects the mindset of the authors for understanding this field as easier as possible and we hope that this book could be a first step into FMER suing color and depth data for its readers. we encourage the reader to contact us with any comments and suggestions for improvement.

Cite As

S. Muhammad Hossein Mousavi (2024). Introduction to Facial Micro Expressions Analysis (https://github.com/SeyedMuhammadHosseinMousavi/Introduction-to-Facial-Micro-Expressions-Analysis-Using-Color-and-Depth-Images-a-Matlab-Coding-Appr), GitHub. Retrieved .

MATLAB Release Compatibility
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APPENDIX C Advanced FER Using Metaheuristics and Neural Gas Networks (NGN)/BEHAlgorithmImageSegmentation

APPENDIX C Advanced FER Using Metaheuristics and Neural Gas Networks (NGN)/NGNImageSegmentation

APPENDIX C Advanced FER Using Metaheuristics and Neural Gas Networks (NGN)/VAOFeatureSelection

APPENDIX C Advanced FER Using Metaheuristics and Neural Gas Networks (NGN)/WDOAImageContrastEnhancement

Introduction to Facial Micro Expressions Analysis Using Color and Depth Images a Matlab Coding Approach

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Version Published Release Notes
2.1.1

The second edition is added.

1.1.3

title change

1.1.2

-Publishing any sample of IKFDB is illegal.

-Samples inside the database could be used just in a scientific experiment and not for any other purposes.
If you used samples of this database in your experiment you have to cite it properly as bellow:

1.1.1

nothing

1.1.0

file change

1.0.0

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.