• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
iConference
 

iConference

on

  • 269 views

 

Statistics

Views

Total Views
269
Views on SlideShare
266
Embed Views
3

Actions

Likes
0
Downloads
1
Comments
0

1 Embed 3

http://webuiltit.net 3

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • The problem we are trying to solve….
  • Right now, there is no easy way to get these answers. Our app hopes to be an integrated platform for users to obtain these kind of information that they need. Research shown that Form stronger memory associationsExample scenario:
  • For app to be useful and popular -> easy to use and fuss-freeWhere to go get the info?People are too busy. Too tedious to fill in data manually
  • Example scenarios: Familiar face, fast forward sessionsAccuracy problems ->search
  • People wear name tags but might be too far to see.Reasonable distance factor is about one room distance.

iConference iConference Presentation Transcript

  • By: Heng Wei Jian
  • Motivation of work Main objective at conference -> Network with other participants Golden opportunity that is often wasted Build a new mobile platform that facilitates this process Finding and presenting essential information to the user using augmented reality technologies
  • Motivation of workIn a conference: Who should I talk to? Who is that guy over there? He seems familiar. How do I find out more about him? How do I get an opportunity to talk to him? How do I approach him?
  • Project Objectives To create a mobile application that enables users to network effectively with other participants in a conference Evaluate the usefulness of the system.
  • ApproachPlatform: iPhone 4Main Features:(Who to talk to?) Real-time mobile Facial Search Conference booklet with QRCodes(How to find out more about him?) Facebook integration(How to contact him?) Real-time personalized message board
  • Research Topics Balance between privacy concerns and ease of use• Find out best possible way to gather information about participants in a conference without intruding their privacy but requiring minimal user input• Can make use of existing social networks to get participants information but needs to be appropriate in a conference context
  • Research Topics (cont) Usefulness of the various user search tools for identifying people. Textual Search Facial Search QR Search
  • Research Topics (cont) Effectiveness of mobile tool for conference networking purposes Evaluation of system  Ease of use  Error frequency  Interface design  Task suitability  Satisfaction  Privacy concerns
  • Technical DesignTools: IOS4.0 PHP remote server MySQL database OpenCV 2.0 Facebook SDK ARPlus toolkit
  • Application Flowchart Facebook Login Login Account Normal Settings Login Conference List Textual Personal Conference Message Search Details board Participant Participant Facial Search List Details QR Search
  • Core Feature: Facial Search Allows user to easily identify other participants in the conference using facial detection and facial recognition technologies Non-intrusive and appropriate in conference context Training images can be obtained from social networks to relieve user manual input Results augmented on screen
  • General Approach Grab image Augment Facial Facialfrom camera results on Detection Recognition frame screen
  • Challenges Running speed on Mobile Devices  Most algorithms require fast CPU speed and high memory Accuracy  Accuracy is heavily dependent on pose and illumination Obtaining Training Images  Get sufficient quality training images without heavy user input Capturing moving images Distance factor
  • Current ProgressiConference
  • Current ProgressFace Detection  OpenCV  Implements Viola-Jones object detection framework  Makes use of Haar Classifier to describe and find general facial features  Accuracy level for frontal view : 95%  Already tested on the phone – average of 1 to 2s
  • Current ProgressFace Recognition1st Method: Face.com->3rd party web-based recognition tool Advantages:  Easy to use  Accuracy level: 70% Disadvantages:  Not open source  Huge overhead to post image to web to get results  Slow
  • Current ProgressFace Recognition2nd Method: Eigenfaces-> Using PCA (Principal Component Analysis) Advantages  Fast  Uses less memory Disadvantages  Build from scratch  Proclaimed accuracy level: 60%
  • Current ProgressImage Pre-processing Techniques Illumination Face Alignment Normalization Original Rotated,Cropped,Resized HistogramEqualized
  • Timeline: Mar Apr May Jun Jul Aug Sep Oct Nov 1 Research and Implementation V Research on QRs current implementation V Implement QR algorithm in ObjectiveC V Implement QR tracking in the booklet WJ System Design and Modelling Building the framework and foundation of the WJ application 2 Integration and Iteration V Ensure Accuracy and Tweaks to QR tracking WJ Adding extra features to ApplicationV & WJ Modifications to application based on tests 3 Usability and ThesisV & WJ Carry out usability tests at conferencesV & WJ Thesis and Technical Paper
  • DEMO
  • Screenshots (Face.com)
  • Screenshots (EigenFaces)
  • Screenshots Original Cropped Greyscale and Equalized resized