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. 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.

Hello all, I am Neha Goel, Technical Lead for AI/Data Science competitions on the MathWorks Student Competition team. MathWorks is excited to support WiDS Datathon 2020 by providing complimentary MATLAB Licenses, tutorials, and getting started resources to each participant. To request your complimentary license, go to the MathWorks site, click the "Request Software" button, and fill out the software request form. You will get your license within 72 business hours. The WiDS Datathon 2020 focuses on patient health through data from MIT's GOSSIS (Global Open Source Severity of Illness Score) initiative.

Create and train a machine learning model by running MATLAB right in your browser. To submit this form, you must accept and agree to our Privacy Policy. We will not sell or rent your personal contact information. See our privacy policy for details. You are already signed in to your MathWorks Account.

Classification Learner lets you perform common supervised learning tasks such as interactively exploring your data, selecting features, specifying validation schemes, training models, and assessing results. You can export classification models to the MATLAB workspace, or generate MATLAB code to integrate models into applications.

Classification Learner lets you perform common supervised learning tasks such as interactively exploring your data, selecting features, specifying validation schemes, training models, and assessing results. You can export classification models to the MATLAB workspace, or generate MATLAB code to integrate models into applications. Choose your country to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

Celestion loudspeakers have powered the performances of many noted guitar and bass players, including legends such as Jimi Hendrix. Deciding whether a loudspeaker is good enough for professional musicians is a lengthy and painstaking process. Each speaker has its own unique sound based on a combination of sonic characteristics, such as midrange character and brightness. Evaluating a musical instrument loudspeaker involves subjective judgement about whether it generates a "good" sound. Only engineers with years of experience can reliably make that decision, and then only after repeated listening to a single loudspeaker and comparing the sounds it produces with those produced by a reference speaker.