1 / 20

EMS: Expression based Mood Sharing for Social Networks

EMS: Expression based Mood Sharing for Social Networks. Md Munirul Haque Mohammad Adibuzzaman Department of Mathematics, Statistics, and Computer Science Marquette University. Outline . Motivation State of the art Classification of models FACS Drawbacks Comparison Open issues

flower
Download Presentation

EMS: Expression based Mood Sharing for Social Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. EMS: Expression based Mood Sharing for Social Networks MdMunirulHaque Mohammad Adibuzzaman Department of Mathematics, Statistics, and Computer ScienceMarquette University

  2. Outline • Motivation • State of the art • Classification of models • FACS • Drawbacks • Comparison • Open issues • System Overview • System Architecture • Implementation

  3. Motivation • Facebook has 500 million active users • twitter has 190 million visitors per month • Number of smart phone users has crossed 45 millions • Many mood applications in FB • My Mood • SpongeBob Mood • The Mood Weather Report • Name and Mood Analyzer • Manual setting

  4. State of the Art • Active Appearance Model (AAM) • Computer Expression Recognition Tool (CERT) • Eigenface, Eigeneye, Eigenlips • Artificial Neural Network (ANN) • Relevance Vector Machine (RVM)

  5. Classification

  6. FACS

  7. FACS

  8. Drawbacks • Reliability on clear frontal image • Out-of-plane head rotation • Right feature selection • Fail to use temporal and dynamic information • Considerable amount of manual interaction • Noise, illumination, glass, facial hair, skin color issues • Computational cost • Mobility • Intensity • Reliability.

  9. Comparison

  10. Eigenface, Eigeneye, Eigenlips Eigenfaces for the training image set

  11. Characteristics • Real Time Mood to Social Media • Location Aware Sharing • Mood Aware Sharing • Mobility • Resources of Behavioral Research • Context Aware Event Manager

  12. Open Issues • Deception of Expression (suppression, amplification, simulation) • Difference in Cultural, Racial, and Sexual Perception • Intensity • Dynamic Features

  13. System Overview

  14. System Architecture Apache Tomcat Container Application Server MATLAB Builder JA MATLAB AXIS2 JAVA Library runs on MCR Expression Detection Script SOAP/Web Service Engine Server HTTP Call WAMP Browser/Mobile PHP Web Server Client FIG: Expression Detection Architecture

  15. Training Database

  16. Web Client

  17. Future Work • Build a Facebook application which will capture the user image using device camera(webcam or mobile camera). • Feed that image to the MATLAB Script and get Expression detected. • Do a survey on the user response of the Facebook application • Increase accuracy • Images- not present in the database • Confusion matrix

  18. Conclusion • Most computational research requires the extensive ability of MATLAB for different computations like image processing, forecasting and other areas. • Using web service to run a MATLAB script will help do research on Computational sciences research.

  19. Q/A • Any question?? • Comments • Suggestion

More Related