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
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 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