Project 2: Object detection by Antiface projections
Description
•
Implement
the Antiface
method for detection.
•
Use
antiface detectors as simple classifier in AdaBoost
method.
Results
The detection results from
both methods should be presented in the form of ROC curve, which shows the
performance of the detection method by changing the threshold that controls the
detection score. The x-axis is the number of true positives (faces found by the
system) divided by the total number of faces in images (from the ground truth).
The y- axis is (1- number of false positive, divided by the total number of
non-face sub-images). False positive is a sub-image that the system detected as
a face but it’s not marked as a face in the ground truth file.
Data sets
The system should be tested
on CBCL face set. This
data set contains images for training and for testing.
Useful Links
Class lecture on object
detection
Slides explaining the
antiface method
Status
Claimed by àøé÷ åéöîï
òéãå éøåùìîé.