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A Smart Classroom Supervision System (Second Presentation)
Transcript of A Smart Classroom Supervision System (Second Presentation)
Marwa MEDDEB Supervisors:
Xuran ZHAO Background subtraction Techniques Objectives
Conclusion & Future Work Face detection Face recognition Background Subtraction Face detection Face recognition Experimental results Static images
Difference (Empty & Full classroom)
Thresholding (Background & Foreground)
Morphological transformations (Smooth & Dilate)
Foreground reconstruction Objectives Reduce False detection Identify present persons Localize the faces Detect faces positions
Mark the faces
Count the persons
Crop and rotate the face
Save the faces for next step Testing & Coding Limits Tests Tests & Interpretations ...
Computing the Database features
Computing the test feature
Identify the closest feature Database LBP features ok ok Error in background subtraction
Small amount of data for training and test
Test images of low resolution Bad performance in face recognition
No detection in particular cases
No false detection
Fast computations Interpretation Conclusion & Future work Collect more data of good quality
More performance tests
Making a real time detection
Prepare a user-machine interface http://clickdamage.com/sourcecode/index.php
http://www.face-rec.org/algorithms/ References Thank you for your attention A Smart Classroom Supervision System 1 2 3 4 6 8 9 10 11 12 13 14 15 Face recognition Local Binary Pattern approach 5 Face detection Haar features
An integral image
AdaBoost machine learning
A cascade classifier 7 September 28, 2012